Classifying Ready-for-Transfer Patients in the Intensive Care Unit Based on Clinical Data

Franz Ehm (), Volkmar Franz, Maic Regner, Udo Buscher, Hanns-Christoph Held and Peter Spieth
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Franz Ehm: Technische Universiät Dresden
Volkmar Franz: Universitätsklinikum Carl Gustav Carus Dresden
Maic Regner: Universitätsklinikum Carl Gustav Carus Dresden
Udo Buscher: Technische Universiät Dresden
Hanns-Christoph Held: Universitätsklinikum Carl Gustav Carus Dresden
Peter Spieth: Universitätsklinikum Carl Gustav Carus Dresden

A chapter in Operations Research Proceedings 2021, 2022, pp 209-215 from Springer

Abstract: Abstract In the intensive care unit (ICU), a common task for clinicians is to choose patients who are ready-for-transfer to a lower ward in order to make limited capacity available for new arrivals. To support this process, we build three predictive models based on historical data from more than 25,000 ICU cases to evaluate patients according to their actual medical state. The decision is modeled as a classification problem to predict the chance of adverse patient outcome defined by ICU-readmission within 72 h or readmission with subsequent exitus. In addition to a screening method based on critical criteria, we propose logistic regression models relying on critical parameter counts and metrical features from measurements, scores, and patient characteristics, respectively. Performance testing using ICU data demonstrates the ability of our approach to assist the process of patient selection for transfer.

Keywords: Intensive care; Patient transfer; Machine learning (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-031-08623-6_32

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