Leverage machine learning to identify key measures in hospital operations management: a retrospective study to explore feasibility and performance of four common algorithms

Abstract

Using ML to identify key hospital operational measures is viable but performance of ML techniques vary considerably. RF performs best among the four techniques in identifying key measures in financial balance. None of the ML techniques seem effective for identifying quality of care measures. ML is suggested as a decision support tool to remind and inspire decision-makers in certain aspects of hospital operations management.

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