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Machine learning-based clinical decision support system for early diagnosis from real-time physiological data
Auckland University of Technology, Auckland, New Zealand.
Auckland University of Technology, Auckland, New Zealand.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-1940-1747
2016 (Engelska)Ingår i: Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc. , 2016, s. 2943-2946, artikel-id 7848584Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This research aims to design a self-organizing decision support system for early diagnosis of key physiological events. The proposed system consists of pre-processing, clustering and diagnostic system, based on self-organizing fuzzy logic modeling. The clustering technique was employed with empirical pattern analysis, particularly when the information available is incomplete or the data model is affected by vagueness, which is mostly the case with medical/clinical data. Clustering module can be viewed as unsupervised learning from a given dataset. This module partitions the patient vital signs to identify the key relationships, patterns and clusters among the medical data. Secondly, it uses self-organizing fuzzy logic modeling for early symptom and event detection. Based on the clustering outcome, when detecting abnormal signs, a high level of agreement was observed between system interpretation and human expert diagnosis of the physiological events and signs. © 2016 IEEE.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc. , 2016. s. 2943-2946, artikel-id 7848584
Nationell ämneskategori
Medicinteknik
Identifikatorer
URN: urn:nbn:se:mdh:diva-37872DOI: 10.1109/TENCON.2016.7848584ISI: 000400378903014Scopus ID: 2-s2.0-85015391910ISBN: 9781509025961 (tryckt)OAI: oai:DiVA.org:mdh-37872DiVA, id: diva2:1171061
Konferens
2016 IEEE Region 10 Conference, TENCON 2016; Marina Bay Sands, Singapore; Singapore; 22 November 2016 through 25 November 2016
Tillgänglig från: 2018-01-05 Skapad: 2018-01-05 Senast uppdaterad: 2019-06-25Bibliografiskt granskad

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