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

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.