Datenbasierte Operationsplanung im Krankenhaus unter Entwicklung und Einbindung einer Machine Learning Plattform

Abstract

This paper gives an overview of the enhancement of planning and control processes in the operating theater of hospitals. The focus here is on the automated prediction of operating times using machine learning methods based on retrospective data. This article deals with the analysis of the data to be obtained, the preparation and preprocessing of this data and the recommendation of suitable algorithms for machine learning. This work was carried out as part of the funded project Platform for Operation Scheduling and Prediction using machine learning (PROSPER).