Zilker, Sandra and Weinzierl, Sven and Kraus, Mathias and Zschech, Patrick and Matzner, Martin (2024) A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis. HEALTH CARE MANAGEMENT SCIENCE, 27 (2). pp. 136-167. ISSN 1386-9620, 1572-9389
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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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | INTENSIVE-CARE; MODEL; Patient pathway; Process prediction; Sepsis; Interpretability; Interpretable machine learning; Interpretation plots; Deep learning |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Informatics and Data Science > Department Information Systems > Chair of Explainable Artificial Inteligence for Business Value Creation (Prof. Dr. Mathias Kraus) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Jan 2026 07:49 |
| Last Modified: | 14 Jan 2026 07:49 |
| URI: | https://pred.uni-regensburg.de/id/eprint/65138 |
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