Zilker S, Weinzierl S, Kraus M, Zschech P, Matzner M (2024)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2024
Book Volume: 27
Pages Range: 136-167
URI: https://link.springer.com/article/10.1007/s10729-024-09673-8#article-info
DOI: 10.1007/s10729-024-09673-8
Open Access Link: https://link.springer.com/content/pdf/10.1007/s10729-024-09673-8.pdf
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.
APA:
Zilker, S., Weinzierl, S., Kraus, M., Zschech, P., & Matzner, M. (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, 136-167. https://doi.org/10.1007/s10729-024-09673-8
MLA:
Zilker, Sandra, et al. "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 (2024): 136-167.
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