Open AccessAssessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions
Author(s) -
Daniel J. Morgan,
Bill Bame,
Paul Zimand,
Patrick M. Dooley,
Kerri A. Thom,
Anthony D. Harris,
Søren M. Bentzen,
W. H. Ettinger,
Stacy Garrett-Ray,
J. Kathleen Tracy,
Yuanyuan Liang
Publication year - 2019
Publication title -
jama network open
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.278
H-Index - 39
ISSN - 2574-3805
DOI - 10.1001/jamanetworkopen.2019.0348
Subject(s) - medicine , receiver operating characteristic , medicaid , emergency medicine , standard score , health care , machine learning , computer science , economics , economic growth
This prognostic study compares standard readmission risk assessment scores with a machine learning score, the Baltimore score, for predicting 30-day unplanned hospital readmissions calculated in real time.
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