TY - GEN
T1 - Using electronic health records to predict severity of condition for Congestive Heart Failure patients
AU - Sideris, Costas
AU - Shahbazi, Behnam
AU - Pourhomayoun, Mohammad
AU - Sarrafzadeh, Majid
AU - Alshurafa, Nabil
N1 - Publisher Copyright:
Copyright © 2014 ACM.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - We propose a novel way to design an analytics engine based exclusively on electronic health records (EHR). We focus our efforts on Congestive Heart Failure (CHF) patients, although our approach could be extended to other chronic conditions. Our goal is to construct statistical models that predict a CHF patient's length of stay and by extension the severity of his/her condition. We show that it is possible to predict length of hospital stay based on physiological data collected from the first day of hospitalization. Using 10-fold cross validation we achieve accurate predictions with a root mean square error of 3.3 days for hospital stays that are less than 15 days in duration. We also propose a clustering of patients that organizes them to risk groups according to their estimated severity of condition.
AB - We propose a novel way to design an analytics engine based exclusively on electronic health records (EHR). We focus our efforts on Congestive Heart Failure (CHF) patients, although our approach could be extended to other chronic conditions. Our goal is to construct statistical models that predict a CHF patient's length of stay and by extension the severity of his/her condition. We show that it is possible to predict length of hospital stay based on physiological data collected from the first day of hospitalization. Using 10-fold cross validation we achieve accurate predictions with a root mean square error of 3.3 days for hospital stays that are less than 15 days in duration. We also propose a clustering of patients that organizes them to risk groups according to their estimated severity of condition.
KW - Electronic health records
KW - Heart failure
KW - Intervention
KW - Readmission
KW - Remote monitoring systems
UR - http://www.scopus.com/inward/record.url?scp=84908691555&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84908691555&partnerID=8YFLogxK
U2 - 10.1145/2638728.2638815
DO - 10.1145/2638728.2638815
M3 - Conference contribution
AN - SCOPUS:84908691555
T3 - UbiComp 2014 - Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
SP - 1187
EP - 1192
BT - UbiComp 2014 - Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PB - Association for Computing Machinery, Inc
T2 - 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014
Y2 - 13 September 2014 through 17 September 2014
ER -