PEACE-Home: probabilistic estimation of abnormal clinical events using vital sign correlations for reliable home-based monitoring

Forkan, A and Khalil, I 2017, 'PEACE-Home: probabilistic estimation of abnormal clinical events using vital sign correlations for reliable home-based monitoring', Pervasive and Mobile Computing, vol. 38, pp. 296-311.


Document type: Journal Article
Collection: Journal Articles

Title PEACE-Home: probabilistic estimation of abnormal clinical events using vital sign correlations for reliable home-based monitoring
Author(s) Forkan, A
Khalil, I
Year 2017
Journal name Pervasive and Mobile Computing
Volume number 38
Start page 296
End page 311
Total pages 16
Publisher Elsevier
Abstract The objective of this study is to develop a probabilistic model for predicting the future clinical episodes of a patient using observed vital sign values prior to the clinical event. Vital signs (e.g. heart rate, blood pressure) are used to monitor a patient’s physiological functions of health and their simultaneous changes indicate a transition of a patient’s health status. If such changes are abnormal then it may lead to serious physiological deterioration. Chronic patients living alone at home die of various diseases due to the lack of an efficient automated system having prior prediction ability. Our developed system can make probabilistic predictions of future clinical events of an unknown patient in real-time using the learned temporal correlations of multiple vital signs from many similar patients. In this paper, Principal Component Analysis (PCA) is used to separate patients with known medical conditions into multiple categories and then Hidden Markov Model (HMM) is adopted for probabilistic classification and prediction of future clinical states. The advantage of using dynamic probabilistic model over static predictor model for solving our problem is analysed by comparing the results obtained from HMM with a neural network based learning model. Both the learning models are trained and evaluated using six vital signs data of 1023 patient records collected from the MIMIC-II database of MIT physiobank archive. The best HMM models are selected using maximum likelihood probabilities and further used in personalized remote monitoring system to forecast the most probable forthcoming clinical states of a continuously monitored patient. The scalable power of cloud computing is utilized for fast learning of various clinical events from large samples. Our results suggest that the developed technique using multiple physiological parameter trends can significantly enhance the traditional home-based monitoring systems in terms of clinical abnormality detections and predictions.
Subject Pattern Recognition and Data Mining
Health Informatics
Keyword(s) Vital signs
Hidden Markov model
Clinical event
Remote monitoring
Ambient assisted living
DOI - identifier 10.1016/j.pmcj.2016.12.009
Copyright notice © 2017 Elsevier B.V. All rights reserved.
ISSN 1574-1192
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