A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions. (18th December 2020)

• Record Type: Journal Article
• Authors: Sutter, Thomas; Roth, Jan A; Chin-Cheong, Kieran; Hug, Balthasar L; Vogt, Julia E
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 4(2021)
• Page Start: 868
• Page End: 873
• Publication Date: 2020-12-18
• Subjects: Digital epidemiology -- disease-specific model -- hospital readmission -- machine learning -- prediction; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa299
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Unplanned hospital readmissions are a burden to patients and increase healthcare costs. A wide variety of machine learning (ML) models have been suggested to predict unplanned hospital readmissions. These ML models were often specifically trained on patient populations with certain diseases. However, it is unclear whether these specialized ML models—trained on patient subpopulations with certain diseases or defined by other clinical characteristics—are more accurate than a general ML model trained on an unrestricted hospital cohort. In this study based on an electronic health record cohort of consecutive inpatient cases of a single tertiary care center, we demonstrate that accurate prediction of hospital readmissions may be obtained by general, disease-independent, ML models. This general approach may substantially decrease the cost of development and deployment of respective ML models in daily clinical routine, as all predictions are obtained by the use of a single model.