TY - GEN
T1 - Predicting 30-day all-cause readmissions from hospital inpatient discharge data
AU - Yang, Chengliang
AU - Delcher, Chris
AU - Shenkman, Elizabeth
AU - Ranka, Sanjay
PY - 2016/11/18
Y1 - 2016/11/18
N2 - Inpatient hospital readmissions for potentially avoidable conditions are problematic and costly. In this paper, we build machine learning models using variables widely available in health claims data to predict patients' 30-day readmission risks at the time of discharge. These models show high predictive power on a U.S. nationwide readmission database. They are also capable of providing interpretable risk factors globally at the population level and locally associated with each single discharge. In addition, we propose a model-agnostic approach to provide confidence for each prediction. Altogether, using models with high predictive power, interpretable risk factors and prediction confidence may enable health care systems to accurately target high-risk patients and prevent recurrent readmissions by accurately anticipating the probability of readmission at the point of care.
AB - Inpatient hospital readmissions for potentially avoidable conditions are problematic and costly. In this paper, we build machine learning models using variables widely available in health claims data to predict patients' 30-day readmission risks at the time of discharge. These models show high predictive power on a U.S. nationwide readmission database. They are also capable of providing interpretable risk factors globally at the population level and locally associated with each single discharge. In addition, we propose a model-agnostic approach to provide confidence for each prediction. Altogether, using models with high predictive power, interpretable risk factors and prediction confidence may enable health care systems to accurately target high-risk patients and prevent recurrent readmissions by accurately anticipating the probability of readmission at the point of care.
UR - http://www.scopus.com/inward/record.url?scp=85006371413&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006371413&partnerID=8YFLogxK
U2 - 10.1109/HealthCom.2016.7749452
DO - 10.1109/HealthCom.2016.7749452
M3 - Conference contribution
AN - SCOPUS:85006371413
T3 - 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016
BT - 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016
Y2 - 14 September 2016 through 17 September 2016
ER -