A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions

In this study based on an electronic health record cohort of consecutive inpatient cases of a single tertiar y 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 ar e obtained by the use of a single model.
Source: Journal of the American Medical Informatics Association - Category: Information Technology Source Type: research