TY - GEN
T1 - Multiple model analytics for adverse event prediction in remote health monitoring systems
AU - Pourhomayoun, Mohammad
AU - Alshurafa, Nabil
AU - Mortazavi, Bobak
AU - Ghasemzadeh, Hassan
AU - Sideris, Konstantinos
AU - Sadeghi, Bahman
AU - Ong, Michael
AU - Evangelista, Lorraine
AU - Romano, Patrick
AU - Auerbach, Andrew
AU - Kimchi, Asher
AU - Sarrafzadeh, Majid
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/10
Y1 - 2014/2/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84949922867&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949922867&partnerID=8YFLogxK
U2 - 10.1109/HIC.2014.7038886
DO - 10.1109/HIC.2014.7038886
M3 - Conference contribution
AN - SCOPUS:84949922867
T3 - 2014 IEEE Healthcare Innovation Conference, HIC 2014
SP - 106
EP - 110
BT - 2014 IEEE Healthcare Innovation Conference, HIC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Healthcare Innovation Conference, HIC 2014
Y2 - 8 October 2014 through 10 October 2014
ER -