Predicting 30-day all-cause readmissions from hospital inpatient discharge data

Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

29 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016
ISBN (Electronic)9781509033706
DOIs
StatePublished - Nov 18 2016
Event18th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2016 - Munich, Germany
Duration: Sep 14 2016Sep 17 2016

Publication series

Name2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016

Conference

Conference18th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2016
Country/TerritoryGermany
CityMunich
Period9/14/169/17/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Health(social science)
  • Health Informatics

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