Multiple model analytics for adverse event prediction in remote health monitoring systems Conference Paper uri icon


  • 2014 IEEE. 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 Heart Failure patients. Our proposed method provides significant improvements in prediction accuracy and performance over single predictive models.

name of conference

  • 2014 Health Innovations and POCT

published proceedings

  • 2014 IEEE Healthcare Innovation Conference (HIC)

author list (cited authors)

  • Pourhomayoun, M., Alshurafa, N., Mortazavi, B., Ghasemzadeh, H., Sideris, K., Sadeghi, B., ... Sarrafzadeh, M.

citation count

  • 16

complete list of authors

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

publication date

  • October 2014