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  • 1.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A Hybrid Case-Based System in Stress Diagnosis and Treatment2012Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Computer-aided decision support systems play anincreasingly important role in clinical diagnosis and treatment.However, they are difficult to build for domains where thedomain theory is weak and where different experts differ indiagnosis. Stress diagnosis and treatment is an example of such adomain. This paper explores several artificial intelligencemethods and techniques and in particular case-based reasoning,textual information retrieval, rule-based reasoning, and fuzzylogic to enable a more reliable diagnosis and treatment of stress.The proposed hybrid case-based approach has been validated byimplementing a prototype in close collaboration with leadingexperts in stress diagnosis. The obtained sensitivity, specificityand overall accuracy compared to an expert are 92%, 86% and88% respectively.

  • 2.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A Multi-Modal Case-Based System for Clinical Diagnosis and Treatment in Stress Management2009Ingår i: / [ed] Delany, S.J., 2009, s. 215-224Konferensbidrag (Refereegranskat)
    Abstract [en]

    A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often base their diagnosis and decision on manual inspection of signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with the manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. A computer system, classifying the sensor signals is one valuable property assisting a clinician. This paper presents a case-based system that assist a clinician in diagnosis and treatment of stress. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques such as textual information retrieval, rule-based reasoning, and fuzzy logic have been combined together with case-based reasoning to enable more reliable and efficient diagnosis and treatment of stress. The performance has been validated implementing a research prototype and close collaboration with the experts. The experimental results suggest that such a system is valuable both for the less experienced clinicians and for experts where the system may be seen as a second option.

  • 3.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    An Overview of three Medical Applications Using Hybrid Case-Based Reasoning2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today more and more patient journals are stored electronically but they are rarely used for more than statistical purpose. In this paper we present an approach where clinical patient journals are used for improved clinical decision making on an individual level. The underlying assumption is that medical staff benefit from comparing a specific patient with similar patient. By comparing symptoms, diagnosis, medication and outcome in an individual level they are able to make more informed decisions at the point of care. This paper presents some parts of our more than ten years research efforts in the area and some of the projects and their underlying hybrid approaches. As a foundation for all our projects we use case-based reasoning (CBR) research in combination with techniques from artificial intelligence, data mining, statistics and search techniques. Three systems are presented in two medical domains 1) decision support for stress diagnosis 2) decision support for stress treatment and 3) decision support for post-operative pain treatment and discuss results and lessons learned.

  • 4.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case studies on the clinical applications using case-based reasoning2012Ingår i: 2012 Federated Conference on Computer Science and Information Systems, FedCSIS 2012, 2012, s. 3-10Konferensbidrag (Refereegranskat)
    Abstract [en]

    Case-Based Reasoning (CBR) is a promising Artificial Intelligence (AI) method that is applied for problem solving tasks. This approach is widely used in order to develop Clinical Decision Support System (CDSS). A CDSS for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSS could function as an expert for a less experienced clinician or as a second option/opinion of an experienced clinician to their decision making task. This paper presents the case studies on 3 clinical Decision Support Systems as an overview of CBR research and development. Two medical domains are used here for the case studies: case-study-1) CDSS for stress diagnosis case-study-2) CDSS for stress treatment and case-study-3) CDSS for postoperative pain treatment. The observation shows the current developments, future directions and pros and cons of the CBR approach. Moreover, the paper shares the experiences of developing 3CDSS in medical domain in terms of case study.

  • 5.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    System Overview on a Clinical Decision Support System for Stress Management2012Ingår i: Proceedings of the ICCBR 2012 Workshops, 2012, s. 111-116Konferensbidrag (Refereegranskat)
    Abstract [en]

    There is an increased need for Clinical Decision Support Systems (CDSS) in the medical community as ICT technology is increasingly used in hospitals as more and more patient data is stored in computers. A CDSS has the potential to play a vital role and bring essential information and knowledge to the clinicians and function as a second opinion in their decision-making tasks. In this paper, a CDSS in stress management is presented where the CDSS can help the clinicians in order to diagnosis and treat stress related disorders. As a foundation for the CDSS, the Case-Based Reasoning (CBR) approach has been used as a core method of the system. The systems also combine other techniques from artificial intelligence in a multimodal manner, such as fuzzy logic, rule-based reasoning and textual information retrieval. In this paper we review our experiences and research efforts while developing the CDSS. The performance of the CDSS shows that the system can be useful both for trainee clinicians as an expert and as well as for senior clinicians as a second option. Moreover, the observation shows the current developments, and pros and cons of the CBR approach.

  • 6.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    The 3 CDSSs: An Overview and Application in Case-Based Reasoning2012Ingår i: The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS), Linköping: Linköping University Electronic Press, 2012, s. 25-32Konferensbidrag (Refereegranskat)
    Abstract [en]

    A computer-aided Clinical Decision SupportSystem (CDSS) for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSScould function as an expert for a less experienced clinician oras a second option/opinion of an experienced clinician to their decision making task. This paper presents 3 clinical DecisionSupport Systems as an overview of case-based reasoning (CBR) research and development. Two medical domains are used here for the case study 1) CDSS for stress diagnosis 2) CDSS for stress treatment and 3) CDSS for post-operative pain treatment.The observation shows the current developments, future direction and pros and cons of the CBR approach. Moreover,the paper shares the experiences of developing 3CDSS in medical domain.

  • 7.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    FUZZY RULE-BASED CLASSIFICATION TO BUILD INITIAL CASE LIBRARY FOR CASE-BASED STRESS DIAGNOSIS2009Ingår i: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009 / [ed] M.H. Hamza, 2009, s. 225-230Konferensbidrag (Refereegranskat)
    Abstract [en]

    Case-Based Reasoning (CBR) is receiving increasedinterest for applications in medical decision support.Clinicians appreciate the fact that the system reasons withfull medical cases, symptoms, diagnosis, actions takenand outcomes. Also for experts it is often appreciated toget a second opinion. In the initial phase of a CBR systemthere are often a limited number of cases available whichreduces the performance of the system. If past cases aremissing or very sparse in some areas the accuracy isreduced. This paper presents a fuzzy rule-basedclassification scheme which is introduced into the CBRsystem to initiate the case library, providing improvedperformance in the stress diagnosis task. Theexperimental results showed that the CBR system usingthe enhanced case library can correctly classify 83% ofthe cases, whereas previously the correctness of theclassification was 61%. Consequently the proposedsystem has an improved performance with 22% in termsof accuracy. In terms of the discrepancy in classificationcompared to the expert, the goodness-of-fit value of thetest results is on average 87%. Thus by employing thefuzzy rule-based classification, the new hybrid system cangenerate artificial cases to enhance the case library.Furthermore, it can classify new problem cases previouslynot classified by the system.

  • 8.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Multi-Modal and Multi-Purpose Case-based Reasoning in the Health Sciences2009Ingår i: PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES / [ed] Leon Trilling et al, Cambridge, UK: WSEAS press , 2009, s. 378-383Konferensbidrag (Refereegranskat)
    Abstract [en]

    Case-based reasoning systems for medical application are increasingly multi-purpose systems and also multi-modal, using a variety of different methods and techniques to meet the challenges from the medical domain. It this paper, some of the recent medical case-based reasoning systems are classified according to their functionality and development properties. It shows how a particular multi-purpose and multi-modal case-based reasoning system solved these challenges. For this a medical case-based reasoning system in the domain of psychophysiology is used. 

  • 9.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A Multi-Module Case Based Biofeedback System for Stress Treatment2011Ingår i: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 51, nr 2, s. 107-115Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Biofeedback is today a recognized treatment method for a number of physical and psychological problems. Experienced clinicians often achieve good results in these areas and their success largely builds on many years of experience and often thousands of treated patients. Unfortunately many of the areas where biofeedback is used are very complex, e.g. diagnosis and treatment of stress. Less experienced clinicians may even have difficulties to initially classify the patient correctly. Often there are only a few experts available to assist less experienced clinicians. To reduce this problem we propose a computer assisted biofeedback system helping in classification, parameter setting and biofeedback training. By adopting a case based approach in a computer-based biofeedback system, decision support can be offered to less experienced clinicians and provide a second opinion to experts. We explore how such a system may be designed and validate the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In a case study we show that the case based biofeedback system outperforms novice clinicians based on a case library of cases authorized by an expert.

  • 10.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A Three Phase Computer Assisted Biofeedback Training System Using Case-Based Reasoning2008Ingår i: Proc. 9th European Conference on Case-based Reasoning, 2008, s. 57-68Konferensbidrag (Refereegranskat)
    Abstract [en]

    Biofeedback is a method gaining increased interest and showing good results for a number of physical and psychological problems. Biofeedback training is mostly guided by an experienced clinician and the results largely rely on the clinician's competence. In this paper we propose a three phase computer assisted sensor-based biofeedback decision support system assisting less experienced clinicians, acting as second opinion for experienced clinicians. The three phase CBR framework is deployed to classify a patient, estimate initial parameters and to make recommendations for biofeedback training by retrieving and comparing with previous similar cases in terms of features extracted. The three phases work independently from each other. Moreover, fuzzy techniques are incorporated into our CBR system to better accommodate uncertainty in clinicians reasoning as well as decision analysis. All parts in the proposed framework have been implemented and primarily validated in a prototypical system. The initial result shows how the three phases functioned with CBR technique to assist biofeedback training. Eventually the system enables the clinicians to allow a patient to train himself/herself unsupervised.

  • 11.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity2008Ingår i: Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity, ISSN 1867-366X, Vol. 1, s. 3-19Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Intelligent analysis of heterogeneous data and information sources for efficient decision support presents an interesting yet challenging task in clinical envi-ronments. This is particularly the case in stress medicine where digital patient re-cords are becoming popular which contain not only lengthy time series measurements but also unstructured textual documents expressed in form of natural languages. This paper develops a hybrid case-based reasoning system for stress di-agnosis which is capable of coping with both numerical signals and textual data at the same time. The total case index consists of two sub-parts corresponding to signal and textual data respectively. For matching of cases on the signal aspect we present a fuzzy similarity matching metric to accommodate and tackle the imprecision and uncertainty in sensor measurements. Preliminary evaluations have revealed that this fuzzy matching algorithm leads to more accurate similarity estimates for improved case ranking and retrieval compared with traditional distance-based matching crite-ria. For evaluation of similarity on the textual dimension we propose an enhanced cosine matching function augmented with related domain knowledge. This is im-plemented by incorporating Wordnet and domain specific ontology into the textual case-based reasoning process for refining weights of terms according to available knowledge encoded therein. Such knowledge-based reasoning for matching of tex-tual cases has empirically shown its merit in improving both precision and recall of retrieved cases with our initial medical databases. Experts in the domain are very positive to our system and they deem that it will be a valuable tool to foster wide-spread experience reuse and transfer in the area of stress diagnosis and treatment.

  • 12.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity2008Ingår i: Transactions on Case-Based Reasoning on Multimedia Data, ISSN 1867-366X, Vol. 1, nr 1, s. 3-19Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Intelligent analysis of heterogeneous data and information sources for efficient decision support presents an interesting yet challenging task in clinical environments. This is particularly the case in stress medicine where digital patient records are becoming popular which contain not only lengthy time series measurements but also unstructured textual documents expressed in form of natural languages. This paper develops a hybrid case-based reasoning system for stress diagnosis which is capable of coping with both numerical signals and textual data at the same time. The total case index consists of two sub-parts corresponding to signal and textual data respectively. For matching of cases on the signal aspect we present a fuzzy similarity matching metric to accommodate and tackle the imprecision and uncertainty in sensor measurements. Preliminary evaluations have revealed that this fuzzy matching algorithm leads to more accurate similarity estimates for improved case ranking and retrieval compared with traditional distance-based matching criteria. For evaluation of similarity on the textual dimension we propose an enhanced cosine matching function augmented with related domain knowledge. This is implemented by incorporating Wordnet and domain specific ontology into the textual case-based reasoning process for refining weights of terms according to available knowledge encoded therein. Such knowledge-based reasoning for matching of textual cases has empirically shown its merit in improving both precision and recall of retrieved cases with our initial medical databases. Experts in the domain are very positive to our system and they deem that it will be a valuable tool to foster widespread experience reuse and transfer in the area of stress diagnosis and treatment.

  • 13.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Folke, Mia
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Intelligent Stress Management System2009Ingår i: Medicinteknikdagarna 2009, 2009Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, in our daily life we are subjected to a wide range of pressures. When the pressures exceed the extent that we are able to deal with then stress is trigged. High level of stress may cause serious health problems i.e. it reduces awareness of bodily symptoms. So, people may first notice it weeks or months later meanwhile the stress could cause more serious effect in the body and health. A difficult issue in stress management is to use biomedical sensor signals in the diagnosis and treatment of stress. This paper presents a case-based system that assists a clinician in diagnosis and treatment of stress. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques such as textual information retrieval, rule-based reasoning (RBR), and fuzzy logic have been combined together with case-based reasoning to enable more reliable and efficient diagnosis and treatment of stress. The performance has been validated implementing a research prototype and close collaboration with experts.

  • 14.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Erik
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-Based Reasoning for Medical and Industrial Decision Support Systems2010Ingår i: Successful Case-based Reasoning Applications, Springer, 2010, s. 7-52Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    The amount of medical and industrial experience and knowledge is rapidly growing and it is almost impossible to be up to date with everything. The demand of decision support system (DSS) is especially important in domains where experience and knowledge grow rapidly. However, traditional approaches to DSS are not always easy to adapt to a flow of new experience and knowledge and may also show a limitation in areas with a weak domain theory. This chapter explores the functionalities of Case-Based Reasoning (CBR) to facilitate experience reuse both in clinical and in industrial decision making tasks. Examples from the field of stress medicine and condition monitoring in industrial robots are presented here to demonstrate that the same approach assists both for clinical applications as well as for decision support for engineers. In the both domains, DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance the performance in a number of different aspects. Textual information retrieval, Rule-based Reasoning (RBR), and fuzzy logic are combined together with CBR to offer decision support to clinicians for a more reliable and efficient management of stress. Agent technology and wavelet transformations are applied with CBR to diagnose audible faults on industrial robots and to package such a system. The performance of the CBR systems have been validated and have shown to be useful in solving such problems in both of these domains.

  • 15.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A Case-Based Retrieval System for Post-Operative Pain Treatment2011Ingår i: / [ed] Petra Perner and Georg Rub, Germany: IBaI , 2011, s. 30-41Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a clinical decision support system based on case-basedretrieval approach to assist physicians in post-operative pain treatment. Here,the cases are formulated by combining regular features and features using anumerical visual analogue scale (NVAS) through a questionnaire. Featureabstraction is done both in problem and outcome description of a case in order toreduce the number of attributes. The system retrieves most similar cases with theiroutcomes. The outcome of each case brings benefits for physicians since it presentsboth severity and fast recovery by the applied treatment in post-operative patients.Therefore, we have introduced a two-layer case structure i.e., solution is the firstlayer and outcome is the second layer that better suits this medical application. Inthe system, the solution presents the treatment and the outcome contains recoveryinformation of a patient, something physicians are interested in, especially the riskof side effects and complications.

  • 16.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A Computer Aided System for Post-operative Pain Treatment Combining Knowledge Discovery and Case-Based Reasoning2012Ingår i: Lecture Notes in Computer Science, vol. 7466, Springer, 2012, s. 3-16Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    The quality improvement for individual postoperative-pain treatment is an important issue. This paper presents a computer aided system for physicians in their decision making tasks in post-operative pain treatment. Here, the system combines a Case-Based Reasoning (CBR) approach with knowledge discovery. Knowledge discovery is applied in terms of clustering in order to identify the unusual cases. We applied a two layered case structure for case solutions i.e. the treatment is in the first layer and outcome after treatment (i.e. recovery of the patient) is in the second layer. Moreover, a 2nd order retrieval approach is applied in the CBR retrieval step in order to retrieve the most similar cases. The system enables physicians to make more informed decisions since they are able to explore similar both regular and rare cases of post-operative patients. The two layered case structure is moving the focus from diagnosis to outcome i.e. the recovery of the patient, something a physician is especially interested in, including the risk of complications and side effects.

  • 17.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Mining Rare Cases in Post-Operative Pain by Means of Outlier Detection2011Ingår i: IEEE Symposium on Signal Processing and Information Technology (ISSPIT) 2011, IEEE , 2011, s. 35-41Konferensbidrag (Refereegranskat)
    Abstract [en]

    Rare cases are often interesting for health professionals, physicians, researchers and clinicians in order to reuse and disseminate experiences in healthcare. However, mining, i.e. identification of rare cases in electronic patient records, is non-trivial for information technology. This paper investigates a number of well-known clustering algorithms and finally applies a 2 nd order clustering approach by combining the Fuzzy C-means algorithm with the Hierarchical one. The approach was used to identify rare cases from 1572 patient cases in the domain of post-operative pain treatment. The results show that the approach enables the identification of rare cases in the domain of post-operative pain treatment and 18% of cases were identified as rare

  • 18.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Mining Rare Cases in Post-Operative Pain by Means of Outlier Detection2011Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Rare cases are often interesting for healthprofessionals, physicians, researchers and clinicians in order toreuse and disseminate experiences in healthcare. However,mining, i.e. identification of rare cases in electronic patientrecords, is non-trivial for information technology. This paperinvestigates a number of well-known clustering algorithms andfinally applies a 2nd order clustering approach by combining theFuzzy C-means algorithm with the Hierarchical one. Theapproach is used in order to identify rare cases from 1572patient cases in the domain of post-operative pain management.The results show that the approach enables identification of rarecases in the domain of post-operative pain management and 18%of cases are identified as rare case.

  • 19.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Olsson, Erik
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    A Case-Based Reasoning System for Knowledge and Experience Reuse2007Ingår i: Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society, 2007, s. 70-80Konferensbidrag (Refereegranskat)
    Abstract [en]

    Experience is one of the most valuable assets technicians and engineer have and may have been collected during many years and both from successful solutions as well as from very costly mistakes. Unfortunately industry rarely uses a systematic approach for experience reuse. This may be caused by the lack of efficient tools facilitating experience distribution and reuse. We propose a case-based approach and tool to facilitate experience reuse more systematically in industry. It is important that such a tool allows the technicians to give the problem case in a flexible way to increase acceptance and use. The proposed tool enables more structured handling of experience and is flexible and can be adapted to different situations and problems. The user is able to input text in a structured way and possibly in combination with other numeric or symbolic features. The system is able to identify and retrieve relevant similar experiences for reuse.

  • 20.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Olsson, Erik
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Efficient Condition Monitoring and Diagnosis Using a Case-Based Experience Sharing System2007Ingår i: The 20th International Congress and Exhibition on Condition Monitoring and Diagnostics Engineering Management, COMADEM 2007, Faro, Portugal, 2007, s. 305-314Konferensbidrag (Refereegranskat)
    Abstract [en]

    Industry has to adjust quickly to changes in their surroundings, for example reducing staff during recession and increasing staff when the market demands it. These factors may cause rapid loss of experience, collected during many years, or require experienced staff to spend considerable resources in training new staff, instead of focusing on production. This is recognised as very costly for companies and organisations today and also reduces competitiveness and productivity. Condition Monitoring, diagnostics and selection of efficient preventive or corrective actions is a task that often requires a high degree of expertise. This expertise is often gained through sometimes very expensive mistakes and can take many years to acquire leading to a few skilled experts. When they are not available due to changes in staff or retirements the company often faces serious problems that may be very expensive, e.g. leading to a reduced productivity.

    If some deviation occurs in a machine, a fault report is often written; an engineer makes a diagnosis and may order spare parts to repair the machine. Fault report, spare parts, required time and statistics on performance after repair are often stored in different databases but so far not systematically reused. In this paper we present a Case-Based experience sharing system that enables reuse of experience in a more efficient way compared with what is mostly practiced in industry today. The system uses Case-Based-Reasoning (CBR) and limited Natural Language Processing. An important aspect of the experience management tool is that it is user-friendly and web-based to promote efficient experience sharing. The system should be able to handle both experiences that are only in house as well as sharing experience with other industries when there is no conflicting interest. Such a CBR based tool enables efficient experience gathering, management and reuse in production industries. The tool will facilitate the users with an interactive environment to communicate with each other for sharing their experiences. Depend on the user; the security level of the system will be varied to share knowledge among the collaborating companies.

    The system identifies the most relevant experiences to assess and resolve the current situation. The experience is stored and retrieved as a case in the collaborative space where experience from various companies may have been stored under many years. Reusing experience and avoiding expensive mistakes will increase the participating companies' competitiveness and also transfer valuable experience to their employees. One of the benefits is also the opportunity and facility to identify people with similar tasks and problems at different companies and enable them to share their experience, e.g. if a technician has solved a similar problem recently and is in the near, the most efficient solution may be to call the expert and ask for assistance. In future, one may access this tool through his/her mobile device via wireless or mobile communications using Global Positioning System, GPS, enables the system to suggest experts nearby, willing and able to share the knowledge and quickly assist in resolve the problem.

  • 21.
    Andersson, Alf
    et al.
    Volvo Car Corporation Manufacturing Engineering.
    Erdem, Ilker
    Chalmers University of Technology.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. IS (Embedded Systems).
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. IS (Embedded Systems).
    Kihlman, Henrik
    ProdTex.
    Bengtsson, Kristofer
    Chalmers University of Technology.
    Falkman, Petter
    Chalmers University of Technology.
    Torstensson, Johan
    Fraunhofer-Chalmers Centre.
    Carlsson, Johan
    Fraunhofer-Chalmers Centre.
    Scheffler, Michael
    Carl Zeiss Automated Inspection GmbH & Co.
    Bauer, Stefan
    Carl Zeiss Automated Inspection GmbH & Co.
    Paul, Joachim
    Carl Zeiss Automated Inspection GmbH & Co.
    Lindkvist, Lars
    Chalmers University of Technology.
    Nyqvist, Per
    Chalmers University of Technology.
    Inline Process Control – a concept study of efficient in-line process control and process adjustment with respect to product geometry2016Ingår i: Swedish Production Symposium 2016 SPS 2016, Lund, Sweden, 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    All manufacturing processes have variation which may violate the fulfillment of assembly, functional, geometrical or esthetical requirements and difficulties to reach desired form in all areas. The cost for geometry defects rises downstream in the process chain. Therefore, it is vital to discover these defects as soon as they appear. Then adjustments can be done in the process without losing products or time. In order to find a solution for this, a project with the overall scope “development of an intelligent process control system” has been initiated. This project consists of five different work packages: Inline measurement, Process Evaluation, Corrective actions, Flexible tooling and demonstrator cell. These work packages address different areas which are necessary to fulfill the overall scope of the project. The system shall both be able to detect geometrical defects, propose adjustments and adjust simple process parameters. The results are demonstrated in a demo cell located at Chalmers University of Technology. In the demonstrator all the different areas have been verified in an industrial case study – assembly of GOR Volvo S80. Efficient offline programming for robot based measurement, efficient process evaluation based on case base reasoning (CBR) methodology, flexible fixtures and process adjustments based on corrective actions regarding in going part positioning.

  • 22.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahlström, Christer
    MFT, Linköping Sweden.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Automated EEG Artifact Handling with Application in Driver Monitoring2017Ingår i: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, nr 5, s. 1350-1361Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.

  • 23.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-Based Systems in the Health Sciences - A Case Study in the Field of Stress Management2009Ingår i: WSEAS Transactions on Systems, ISSN 1109-2777, Vol. 8, nr 3, s. 344-354Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Now-a-days medical domain is a popular area for the artificial intelligence (AI) research. Many of the early AI systems were attempted to apply rule-based reasoning in developing computer-based diagnosis system in medical domain. However, for a broad and complex medical domain the effort of applying rule-based system has encountered several problems. Today many systems are serving multi-purpose i.e. tend to support not only in diagnosis but also in number of other complex tasks and combining more than one AI techniques in the health care domain. In this paper, we will investigate the state-of-the art of casebased reasoning (CBR), a recent AI method in the medical domain. A case study in the stress medicine domain is presented here. Today stress has become a major concern in our society. The demand of the decision support system (DSS) in stress domain is increasing rapidly. However, the application of DSS in this domain is limited so far due to the weak domain theory. In our on going research, we have proposed a solution analyzing the relation between stress and finger temperature using case-based reasoning and other AI techniques namely case-based reasoning, textual CBR, rule-based reasoning, and fuzzy logic to support classification and diagnosis in stress management.

  • 24.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    ECG Sensor Signal Analysis to Represent Cases in a Case-based Stress Diagnosis System2010Ingår i: Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB, Corfu, Greece, 2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) sensor signal. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress using ECG sensor signal.

  • 25.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Physiological Sensor Signals Analysis to Represent Cases in a Case-based Diagnostic System2013Ingår i: Innovations in Knowledge-based Systems in Biomedicine, vol. 250 / [ed] Pham T.D,Jain L.C., Springer, 2013, s. 1-25Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    Today, medical knowledge is expanding so rapidly that clinicians cannot follow all progress any more. This is one reason for making knowledge- based systems desirable in medicine. Such systems can give a clinician a second opinion and give them access to new experience and knowledge. Recent advances in Artificial Intelligence (AI) offers methods and techniques with the potential of solving tasks previously difficult to solve with computer-based systems in medical domains. This chapter is especially concerned with diagnosis of stress-related dysfunctions using AI methods and techniques. Since there are large individual variations between people when looking at biological sensor signals to diagnose stress, this is a worthy challenge. Stress is an inevitable part of our human life. No one can live without stress. However, long-term exposure to stress may in the worst case cause severe mental and/or physical problems that are often related to different kind of psychosomatic disorders, coronary heart disease etc. So, diagnosis of stress is an important issue for health and well-being. Diagnosis of stress often involves acquisition of biological signals for example finger temperature, electrocardiogram (ECG), electromyography (EMG) signal, skin conductance (SC) signals etc. and is followed by a careful analysis by an expert. However, the number of experts to diagnose stress in psycho-physiological domain is limited. Again, responses to stress are different for different persons. So, interpreting a particular curve and diagnosing stress levels is difficult even for experts in the domain due to large individual variations. It is a highly complex and partly intuitive process which experienced clinicians use when manually inspecting biological sensor signals and classifying a patient. Clinical studies show that the pattern of variation within heart rate i.e., HRV signal and finger temperature can help to determine stress-related disorders. This chapter presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) and finger temperature sensor signals. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress.

  • 26.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Filla, Reno
    Driver's Mental State Monitoring System Using CBR Based on Heart Rate Variability Analysis2012Ingår i:  , 2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. This is especially important for professional drivers since both expensive equipment and lives may be at stake, e.g. in mining, construction and personal transportation, reduced concentration, stress or tiredness are known to be the cause of many accidents. A system which recognizes the state of the driver and e.g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. Today different sensors enable clinician to determine a driver’s status with high accuracy. The aim of the paper is to develop an intelligent system that can monitor drivers’ stress depending on psychological and behavioral conditions/status using heart rate variability. An experienced clinician is able to diagnose a person’s stress level based on sensor readings. Here, we propose a solution using case-based reasoning to diagnose individual driver’s stress. During calibration a number of individual parameters are established. The system also considers the feedback from the driver’s on how well the test was performed The validation of the approach is based on close collaboration with experts and measurements from 18 driver’s from Volvo Construction Equipment are used as reference.

  • 27.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Filla, Reno
    Volvo.
    Mental State Monitoring System for the Professional Drivers Based on Heart Rate Variability Analysis and Case-based Reasoning2012Ingår i: 2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), NEW YORK: IEEE , 2012, s. 35-42Konferensbidrag (Refereegranskat)
    Abstract [en]

    The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. A system that recognizes the state of the driver and e. g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. So, the aim of the project is to develop an intelligent system that can monitor drivers' stress depending on psychological and behavioral conditions/status using Heart Rate Variability (HRV). Here, we have proposed a solution using Case-Based Reasoning (CBR) to diagnose individual driver's level of stress. The system also considers feedback from the driver's on how well the test was performed. The validation of the approach is based on close collaboration with experts and measurements from 18 drivers from Volvo Construction Equipment (Volvo CE) are used as reference.

  • 28.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    An Overview on Recent Case-Based Reasoning Systems in the Medicine2009Ingår i: 25th annual workshop of the Swedish Artificial Intelligence Society, 2009Konferensbidrag (Refereegranskat)
    Abstract [en]

    Case-based reasoning systems for medical application are increasingly applied to meet the challenges from the medical domain. This paper looks at the state of the art in case-based reasoning and some systems are classified in this respect. A survey is performed based on the recent publications and research projects in CBR in medicine. Also, the survey is based on e-mail questionnaire to the authors’ to complete the missing property information. Some clear trends in recent projects/systems have been identified such as most of the systems are multi-modal, using a variety of different methods and techniques to serve multipurpose i.e. address more than one task.

  • 29.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Folke, Mia
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments2011Ingår i: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews, ISSN 1094-6977, E-ISSN 1558-2442, Vol. 41, nr 4, s. 421-434Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The Health Sciences are, nowadays, one of the major application areas for case-based reasoning (CBR). The paper presents a survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented. Some clear trends have been identified, such as multipurpose systems: more than half of the current medical CBR systems address more than one task. Research on CBR in the area is growing, but most of the systems are still prototypes and not available on the market as commercial products. However, many of the projects/systems are intended to be commercialized.

  • 30.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Folke, Mia
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    A computer-based system for the assessment and diagnosis of individual sensitivity to stress in Psychophysiology2007Konferensbidrag (Refereegranskat)
    Abstract [en]

    Increased exposure to stress may cause serious health problems leading to long term sick leave if undiagnosed and untreated. The practice amongst clinicians' to use a standardized procedure measuring blood pressure, ECG, finger temperature, breathing speed etc. to make a reliable diagnosis of stress and stress sensitivity is increasing. But even with these measurements it is still difficult to diagnose due to large individual variations. A computer-based system as a second option for the assessment and diagnosis of individual stress level is valuable in this domain.

    A combined approach based on a calibration phase and case-based reasoning is proposed exploiting data from finger temperature sensor readings from 24 individuals. In calibration phase, a standard clinical procedure with six different steps helps to establish a person's stress profile and set up a number of individual parameters. When acquiring a new case, patients are also asked to provide a fuzzy evaluation on how reliable was the procedure to define the case itself. Such a reliability "level" could be used to further discriminate among similar cases. The system extracts key features from the signal and classifies individual sensitivity to stress. These features are stored into a case library and similarity measurements are taken to assess the degrees of matching and create a ranked list containing the most similar cases retrieved by using the nearest-neighbor algorithm.

    A current case (CC) is compared with two other stored cases (C_92 and C_115) in the case library. The global similarity between the case CC and case C_92 is 67% and case CC and case C_115 is 80% shown by the system. So the case C_115 has ranked higher than the case C_92 and is more similar to current case CC. If necessary, the solution for the best matching case can be revised by the clinician to fit the new patient. The current problem with confirmed solution is then retained as a new case and added to the case library for future use.

    The system allows us to utilize previous experience and at the same time diagnose stress along with a stress sensitivity profile. This information enables the clinician to make a more informed decision of treatment plan for the patients. Such a system may also be used to actively notify a person's stress levels even in the home environment.

  • 31.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Schéele, Bo von
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching2009Ingår i: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 25, nr 3, s. 180-195Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Stress diagnosis based on finger temperature signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret and analyze complex, lengthy sequential measurements in order to make a diagnosis and treatment plan. The paper presents a case-based decision support system to assist clinicians in performing such tasks. Case-based reasoning is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness-of-fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation which shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with case-based reasoning is a valuable approach in domains where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho-physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.

  • 32.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning2007Ingår i: Case-Based Reasoning Research and Development: 7th International Conference on Case-Based Reasoning, ICCBR 2007 Belfast, Northern Ireland, UK, August 13-16, 2007 Proceedings, Springer, 2007, s. 478-491Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Increased exposure to stress may cause health problems. An experi-enced clinician is able to diagnose a person's stress level based on sensor read-ings. Large individual variations and absence of general rules make it difficult to diagnose stress and the risk of stress-related health problems. A decision sup-port system providing clinicians with a second opinion would be valuable. We propose a novel solution combining case-based reasoning and fuzzy logic along with a calibration phase to diagnose individual stress. During calibration a num-ber of individual parameters are established. The system also considers the feedback from the patient on how well the test was performed. The system uses fuzzy logic to incorporating the imprecise characteristics of the domain. The cases are also used for the individual treatment process and transfer experience between clinicians. The validation of the approach is based on close collabora-tion with experts and measurements from 24 persons used as reference.

  • 33.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Individualized Stress Diagnosis Using Calibration and Case-Based Reasoning2007Ingår i: Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society, Borås, Sweden, 2007, s. 59-69Konferensbidrag (Refereegranskat)
    Abstract [en]

    Diagnosing stress is difficult even for experts due to large individual variations. Clinician's use today manual test procedures where they measure blood pressure, ECG, finger temperature and breathing speed during a number of exercises. An experienced clinician makes diagnosis on different readings shown in a computer screen. There are only very few experts who are able to diagnose and predict stress-related problems. In this paper we have proposed a combined approach based on a calibration phase and case-based reasoning to provide assistance in diagnosing stress, using data from the finger temperature sensor readings. The calibration phase helps to establish a number of individual parameters. The system uses a case-based reasoning approach and also feedback on how well the patient succeeded with the different test, used for giving similar cases reliability estimates.

  • 34.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Similarity of Medical Cases in Health Care Using Cosine Similarity and Ontology2007Konferensbidrag (Refereegranskat)
    Abstract [en]

    The increasing use of digital patient records in hospital saves both time and reduces risks wrong treatments caused by lack of information. Digital patient records also enable efficient spread and transfer of experience gained from diagnosis and treatment of individual patient. This is today mostly manual (speaking with col-leagues) and rarely aided by computerized system. Most of the content in patient re-cords is semi-structured textual information. In this paper we propose a hybrid tex-tual case-based reasoning system promoting experience reuse based on structured or unstructured patient records, case-based reasoning and similarity measurement based on cosine similarity metric improved by a domain specific ontology and the nearest neighbor method. Not only new cases are learned, hospital staff can also add comments to existing cases and the approach enables prototypical cases.

  • 35.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress2006Ingår i: 8th European Workshop on Case-based Reasoning in the Health Sciences, workshop proceedings, 2006, s. 113-122Konferensbidrag (Refereegranskat)
    Abstract [en]

    In the medical literature there are a number of physiological reactions related to cognitive activities. Psychosocial and psychophysiological stress is such activities reflected in physiological reactions. Stress related symptoms are highly individual, but decreased hands temperature is the common for most individuals. A clinician learns with experience how to interpret the different symptoms but there is no adaptive diagnostic system for diagnosing stress. Decision support systems (DSS) diagnosing stress would be valuable both for junior clinicians and as second opinion for experts. Due to the large individual variations and no general set of rules, DSS are difficult to build for this task. The proposed solution combines a calibration phase with case-based reason¬ing approach and fuzzification of cases. During the calibration phase a number of individual parameters and case specific fuzzy membership functions are es-tablishes. This case-based approach may help the clinician to make a diagnosis, classification and treatment plan. The case may also be used to follow the treat-ment progress. This may be done using the proposed system. Initial tests show promising results. The individual cases including calibration and fuzzy mem-bership functions may also be used in an autonomous system in home environ-ment for treatment programs for individuals often under high stress.

  • 36.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Folke, Mia
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Diagnosis and Biofeedback System for Stress2009Ingår i: Proceedings of the 6th International Workshop on Wearable, Micro, and Nano Technologies for Personalized Health: "Facing Future Healthcare Needs", pHealth 2009, 2009, s. 17-20Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, everyday life for many people contain many situations that may trigger stress or result in an individual living on an increased stress level under long time. High level of stress may cause serious health problems. It is known that respiratory rate is an important factor and can be used in diagnosis and biofeedback training, but available measurement of respiratory rate are not especially suitable for home and office use. The aim of this project is to develop a portable sensor system that can measure the stress level, during everyday situations e.g. at home and in work environment and can help the person to change the behaviour and decrease the stress level. The sensor explored is a finger temperature sensor. Clinical studies show that finger temperature, in general, decreases with stress; however this change pattern shows large individual variations. Diagnosing stress level from the finger temperature is difficult even for clinical experts. Therefore a computer-based stress diagnosis system is important. In this system, case-based reasoning and fuzzy logic have been applied to assists in stress diagnosis and biofeedback treatment utilizing the finger temperature sensor signal. An evaluation of the system with an expert in stress diagnosis shows promising result.

  • 37.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Erik
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Development of a Stress Questionnaire: A Tool for Diagnosing Mental Stress2010Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Stress and its relation with health, behavioral and environmental factors are known today. The stress questionnaire is a scientific screening instrument to understand individual’s causes of stress in different parts of life e.g. in the work place and at home. The 38-item stress questionnaire (SQ) is developed to assess the appraisal of stress personally experienced in a patient’s life. This questionnaire cannot diagnose any illness or psychological disorder. However it can be a helpful tool for developing the individual stress management plan by assessing data about the current demands of individual’s life and work.

  • 38.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management2010Ingår i: Computational Intelligence in Healthcare 4: Advanced Methodologies / [ed] Isabelle Bichindaritz et. al., Springer Berlin/Heidelberg, 2010, s. 159-189Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    The complexity of modern lifestyle and society brings many advantages but also causes increased levels of stress for many people. It is known that increased exposure to stress may cause serious health problems if undiagnosed and untreated and a report from the Swedish government estimates that 1/3 of all long term sick leave is stress related. One of the physiological parameters for quantifying stress levels is the finger temperature that helps the clinician in diagnosis and treatment of stress. However, in practice, the complex and varying nature of signals makes it difficult and tedious to interpret and analyze the lengthy sequential measurements. A computer based system diagnosing stress would be valuable both for clinicians and for treatment. Since stress diagnosis has a week domain theory and there are large individual variations, Case-Based Reasoning is proposed as the main methodology. Feature extraction methods abstracting the original signals without losing key features are investigated. A fuzzy technique is also incorporated into the system to perform matching between the features derived from signals to better accommodate vagueness, uncertainty often present in clinical reasoning Validation of the approach is based on close collaboration with experts and measurements from twenty four persons. The system formulates a new problem case with 17 extracted features from the fifteen minutes (1800 samples) of biomedical sensor data. Thirty nine time series from twenty four persons have been used to evaluate the approach (matching algorithms) in which the system shows a level of performance close to an experienced expert. The system can be used as an expert for a less experienced clinician, as a second option for an experienced clinician or for treatment in home environment.

  • 39.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Fusion Based System for Physiological Sensor Signal Classification2014Ingår i: Medicinteknikdagarna 2014 MTD10, 2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, usage of physiological sensor signals is essential in medical applications for diagnoses and classification of diseases. Clinicians often rely on information collected from several physiological sensor signals to diagnose a patient. However, sensor signals are mostly non-stationary and noisy, and single sensor signal could easily be contaminated by uncertain noises and interferences that could cause miscalculation of measurements and reduce clinical usefulness. Therefore, an apparent choice is to use multiple sensor signals that could provide more robust and reliable decision. Therefore, a physiological signal classification approach is presented based on sensor signal fusion and case-based reasoning. To classify Stressed and Relaxed individuals from physiological signals, data level and decision level fusion are performed and case-based reasoning is applied as classification algorithm. Five physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, data level fusion is performed using Multivariate Multiscale Entropy (MMSE) and extracted features are then used to build a case- library. Decision level fusion is performed on the features extracted using traditional time and frequency domain analysis. Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

  • 40.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Islam, Mohd. Siblee
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    K-NN Based Interpolation to Handle Artifacts for Heart Rate Variability Analysis2011Ingår i: IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011, IEEE , 2011, s. 387-392Konferensbidrag (Refereegranskat)
    Abstract [en]

    Heart rate variability (HRV) is a popular parameter for depicting activities of autonomous nervous system and helps to explain various physiological activities of the body. A small amount of artifacts can produce significant changes especially, for time domain HRV features. Manual correction of artifacts performed by visual inspection of the signal by experts is tedious and time consuming and often leads to incorrect result especially for long term recordings. Therefore, an automatic artifact removing approach that helps to provide clinically useful HRV analysis is valuable. This paper proposes an algorithm that detects and replaces artifacts from inter-beat interval (IBI) signal for HRV analysis. The detection is mainly based on windowing technique and interpolation is performed using the k-nearest neighbour (K-NN) algorithm. The experimental work shows a promising performance in handling artifacts for HRV analysis using electrocardiogram (ECG) sensor signal.

  • 41.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik. Dalarna University, Borlänge, Sweden .
    Westin, Jerker
    Dalarna University, Borlänge, Sweden .
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Dougherty, Mark
    Dalarna University, Borlänge, Sweden .
    Induction of an Adaptive Neuro-Fuzzy Inference System for Investigating Fluctuation in Parkinson´s Disease: The 23rd Annual Workshop of the Swedish Artificial Intelligence Society Umeå, May 10-12, 20062006Ingår i: Proceedings of SAIS 2006, 2006, s. 67-72Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge, supported by prior knowledge and statistical modeling. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to "discover" relationships between fluctuation, treatment and disease severity in Parkinson. Preliminary results from this project are promising and show that Neuro-fuzzy techniques in combination with statistical methods may offer medical research and medical applications a useful combination of methods.

  • 42.
    Bengtsson, Marcus
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Fundin, Anders
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Deleryd, Mats
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Salonen, Antti
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Erik
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Andersson, Carina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Qureshi, Hassan
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Integrating Quality and Maintenance Development: Opportunities and Implications2010Ingår i: Proceedings of the 23rd International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2010): Advances in Maintenance and Condition Diagnosis Technologies towards Sustainable Society / [ed] S. Okumura, T. Kawai, P. Chen, and R. B. K. N. Rao, 2010, s. 821-828Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, the drive in many organizations is to focus on reducing production costs while increasing customer satisfaction. One key to succeed with these goals is to develop and improve both quality and maintenance in production as well as quality and maintenance in early phases of the development processes. The purpose of this paper is to discuss how and motivate why research within quality and maintenance development may interact, in order to help companies meet customer demand while at the same time increase productivity. The paper is based on ideas and research perspectives of the newly formed competence group on ‘Quality- and Maintenance Development’ at the School of Innovation, Design and Engineering at the Malardalen University, Sweden. This paper elaborates on the concepts of Quality and Maintenance, its important integration, and provides some examples of ongoing research projects within the competence group.

  • 43.
    Bengtsson, Marcus
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Erik
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Jackson, Mats
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Technical Design of Condition Based Maintenance Systems - A Case Study Using Sound Analysis and Case-Based Reasoning2004Konferensbidrag (Refereegranskat)
    Abstract [en]

    Productivity is a key weapon for manufacturing companies to stay competitive in a continuous growing global market. Increased productivity can be achieved through increased availability. This has directed focus on different maintenance types and maintenance strategies. Increased availability through efficient maintenance can be achieved through less corrective maintenance actions and more accurate preventive maintenance intervals. Condition Based Maintenance (CBM) is a technology that strives to identify incipient faults before they become critical which enables more accurate planning of the preventive maintenance. CBM can be achieved by utilizing complex technical systems or by humans manually monitoring the condition by using their experience, normally a mixture of both is used. Although CBM holds a lot of benefits compared to other maintenance types it is not yet commonly utilized in industry. One reason for this might be that the maturity level in complex technical CBM system is too low. This paper will acknowledge this possible reason, although not trying to resolve it, but focusing on system technology with component strategy and an open approach to condition parameters as the objective is fulfilled. This paper will theoretically discuss the technical components of a complete CBM system approach and by a case study illustrate how a CBM system for industrial robot fault detection/diagnosis can be designed using the Artificial Intelligence method Case-Based Reasoning and sound analysis.

  • 44.
    Bengtsson, Marcus
    et al.
    Mälardalens högskola, Institutionen för innovation, design och produktutveckling.
    Olsson, Erik
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Jackson, Mats
    Mälardalens högskola, Institutionen för innovation, design och produktutveckling.
    Technical Design of Condition Based Maintenance Systems: A Case Study using Sound Analysis and Case-Based Reasoning2004Konferensbidrag (Övrigt vetenskapligt)
  • 45.
    Bergman, Jan E. S.
    et al.
    Swedish Institute of Space Physics, Sweden.
    Bruhn, Fredrik
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Isham, Brett
    Interamerican University of Puerto Rico, Bayamón, Puerto Rico.
    Rincon-Charris, Amilcar
    Interamerican University of Puerto Rico, Bayamón, Puerto Rico.
    Capo-Lugo, Pedro
    NASA Marshall Space Flight Center, Huntsville, Alabama, USA.
    Åhlen, Lennart
    Swedish Institute of Space Physics, Sweden.
    Exploiting Artificial Intelligence for Analysis and Data Selection on-board the Puerto Rico CubeSat2015Konferensbidrag (Refereegranskat)
  • 46.
    Elfving, Sofi
    et al.
    Mälardalens högskola, Institutionen för innovation, design och produktutveckling.
    Funk, Peter
    Enabling Knowledge Transfer in Product Development and Production: Methods and Techniques From Artificial Intelligence2006Konferensbidrag (Refereegranskat)
  • 47.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Second Generation Intelligen Sensor Systems2006Konferensbidrag (Refereegranskat)
    Abstract [en]

    There is an increased market demand of "smart" sensor systems, both from system and product developers as well as end users. The first generation intelligent sensor systems are sensors with some limited processing capacity that may be used for processing or compressing data, or sending data, calculating average etc. Our proposed definition of the second generation intelligent sensors are that they are capable of behaviour, that a human would classify as intelligent if seen in s sensor. This functionality may be integrated into the hardware, or in the sensors control program. <br><br>

    Example of such functionality may be to identify if the sensor is fully functional and self-calibrating properties. The sensor may also send confidence estimation on how confident it is in the current sensor readings. The sensor may also learn to recognise different internal and external disturbances, e.g. learn how the signal of a close mobile phone influences the sensor readings and correct the readings. Some sensors may also have delegated responsibilities, e.g. turn some sensitive equipment of if they detect some serious conditions needing immediate action, and where a human or centralized response would not be able to arrive in time. This could be to lower the clock speed to avoid overheating. <br><br>

    If sensors are equipped with communication capabilities then an intelligent sensor could be classified as an agent. Wooldridge and Jennings (1995) defines agents to be computer systems (hardware and software, able to observe its environment and influence its environment) that have properties such as: <br>

    • autonomy<br>

    • social abilities<br>

    • reactivity and pro-activeness<br>

    This does not necessarily mean that they have to be designed and implemented with different methods than today. Methods and techniques from artificial intelligent (AI), such as agents or learning systems are today implemented with main steam methods and techniques. The difference is what the requirements are and it is a different way of thinking, often opening a door to new solutions, not always thought of when taking an incremental approach to improvement and extended functionality. <br>

    Suggested important properties in an agent based approach to sensors are: <br>

    1. flexibility and decentralised decision making. <br>

    2. localized learning and experience reuse. <br>

    3. learning and experience sharing between agents with similar tasks. <br>

    4. ability to collaborate with other agents or even humans<br>

    This functionality could be implemented both in hardware or software. An interesting question is how a sensor handles feedback, both positive and negative. Other interesting opportunities arise when sensors communicate, e.g. sensors may have limited knowledge on their functionality and relation. This enables an intelligent sensor to verify its own functionality by comparing its own readings with other sensor readings. Also learning optimal intervals for cleaning or recalibrations may be such an option. If these sensors are part of a complex centrally controlled process, the process may preserve some basic behaviour if the central control process is experiencing some dysfunctions. <br><br>

    Methods and techniques from artificial intelligence area already widely used in many areas but also offer interesting and potential valuable benefits also to areas not traditionally thought of when speaking of AI, e.g. microsystems.

  • 48.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Why clinical decision support systems are a main key to improved health care2009Ingår i: Medicinteknikdagarna 2009, Vasteras, Sweden, 2009Konferensbidrag (Refereegranskat)
  • 49.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Why hybrid case-based reasoning will change the future of health science and healthcare2015Ingår i: CEUR Workshop Proceedings, 2015, s. 199-204Konferensbidrag (Refereegranskat)
    Abstract [en]

    The rapid development of the medical field makes it impossible even for experts in the field to keep up with new treatments and experience. Already in 2010 all medical knowledge doubled in 3,5 years, to keep up to date with all development even in a narrow field is today far beyond human capacity. The need for decision support is increasingly important to ensure optimal treatment of patients, especially if patients are not "standard patients" matching a gold standard treatment. By ensuring confidentiality and collecting structured cases on a large scale will enable clinical decision support far beyond what is possible today and will be a major leap in healthcare. 

  • 50.
    Funk, Peter
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Jackson, Mats
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Experience based diagnostic and condition based maintenence within production systems2005Ingår i: Proceedings of COMADEM 2005, 2005Konferensbidrag (Refereegranskat)
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