A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis

Sandra Zilker (), Sven Weinzierl (), Mathias Kraus (), Patrick Zschech () and Martin Matzner ()
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Sandra Zilker: Professorship for Business Analytics
Sven Weinzierl: Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair of Digital Industrial Service Systems
Mathias Kraus: University of Regensburg, Chair for Explainable AI in Business Value Creation
Patrick Zschech: Leipzig University, Professorship for Intelligent Information Systems and Processes
Martin Matzner: Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair of Digital Industrial Service Systems

Health Care Management Science, 2024, vol. 27, issue 2, No 2, 136-167

Abstract: Abstract Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient’s complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance – where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees – and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.

Keywords: Patient pathway; Process prediction; Sepsis; Interpretability; Interpretable machine learning; Interpretation plots; Deep learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10729-024-09673-8

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