Multiple Model Analytics for Adverse Event Prediction in Remote Health Monitoring Systems

Pourhomayoun, Mohammad ;   Alshurafa, Nabil ;   Mortazavi, Bobak ;   Ghasemzadeh, Hassan ;   Sideris, Konstantinos ;   Sadeghi, Bahman ;   Ong, Michael ;   Evangelista, Lorraine ;   Romano, Patrick ;   Auerbach, Andrew ;   Kimchi, Asher ;   Sarrafzadeh, Majid

Remote health monitoring systems (RHMS) are gaining an important role in healthcare by collecting and transmitting patient vital information and providing data analysis and medical adverse event prediction (e.g. hospital readmission prediction). Reduction in the readmission rate is typically achieved by early prediction of the readmission based on the data collected from RHMS, and then applying early intervention to prevent the readmission. Given the diversity of patient populations and the continuous nature of patient monitoring, a single static predictive model is insufficient for accurately predicting adverse events. To address this issue, we propose a multiple prediction modeling technique that includes a set of accurate prediction models rather than one single universal predictor. In this paper, we propose a novel analytics framework based on the physiological data collected from RHMS, advanced clustering algorithms and multiple-model-classification. We tested our proposed method on a subset of data collected through a remote health monitoring system from 600 Congestive Heart Failure patients. Our proposed method provides significant improvements in prediction accuracy and performance over single predictive models.