Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/315266 
Year of Publication: 
2024
Citation: 
[Journal:] Health Care Management Science [ISSN:] 1572-9389 [Volume:] 27 [Issue:] 2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 136-167
Publisher: 
Springer US, New York, NY
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.
Subjects: 
Patient pathway
Process prediction
Sepsis
Interpretability
Interpretable machine learning
Interpretation plots
Deep learning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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