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  • 1.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering.
    Schéele, Bo von
    Mälardalen University, School of Innovation, Design and Engineering.
    A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching2009In: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 25, no 3, p. 180-195Article in journal (Refereed)
    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.

  • 2.
    Funk, Peter
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    Case Based Reasoning and Knowledge Discovery in Medical Applications with Time Series2006In: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 22, no 3/4, p. 238-253Article in journal (Refereed)
    Abstract [en]

    This paper discusses the role and integration of knowledge discovery (KD) in case-based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case-specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co-occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical application and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co-occurrence between certain sequences and classes.

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