MACHINE LEARNING-BASED CLINICAL DECISION SUPPORT SYSTEM

Abstract

This article aims to design a self-organizing decision support system for early detection of important physiological events. The proposed system consists of pre-processing, clustering and diagnostic systems based on self-organizing fuzzy logic modelling. Clustering methods have been used in empirical pattern analysis. Especially when the available information is incomplete or the data model is subject to ambiguity. This applies primarily to medical/clinical data. A clustering engine can be viewed as unsupervised learning from a given data set. This module analyses a patient's vital signs to identify important relationships, patterns, and clusters among medical data. Then, use self-organizing fuzzy logic modelling to detect symptoms and events early. Based on the clustering results, we observed a high degree of agreement between the system's interpretation and the human expert's diagnosis of physiological events and signs in abnormal sign detection. Keywords: Patient monitoring; Early diagnosis of clinical events; Clinical decision support system; Self-organising fuzzy system; Machine learning; Clustering analysis.