Hospital Readmission is Highly Predictable from Deep Learning
Damien Échevin (),
Qing Li and
Cahiers de recherche from Chaire de recherche Industrielle Alliance sur les enjeux économiques des changements démographiques
Hospital readmission is costly and existing models are often poor or moderate in predicting readmission. We sought to develop and test a method that can be applied generally by hospitals. Such a tool can help clinicians identify patients who are more likely to be readmitted, either at early stages of hospital stay or at hospital discharge. Relying on state-of-the art machine learning algorithms, we predict probability of 30-day readmission at hospital admission and at hospital discharge using administrative data on 1,633,099 hospital stays from Quebec between 1995 and 2012. We measure performance of the predictions with the area under receiver operating characteristic curve (AUC). Deep Learning produced excellent prediction of readmission province-wide, and Random Forest reached very similar level. The AUC for these two algorithms reached above 78% at hospital admission and above 87% at hospital discharge, and the diagnostic codes are among the most predictive variables. The ease of implementation of machine learning algorithms, together with objectively validated reliability, brings new possibilities for cost reduction in the health care system.
Keywords: Machine learning; Logistic regression; Risk of re-hospitalisation; Healthcare costs (search for similar items in EconPapers)
JEL-codes: I10 C52 C55 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp and nep-hea
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Persistent link: https://EconPapers.repec.org/RePEc:lvl:criacr:1705
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