A machine learning framework for interpretable predictions in patient pathways: the case of predicting ICU admission for patients with symptoms of sepsis
Open accessAbstract
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.
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Perceived usefulness of XAI systems was reported to be user or context dependent.
“Perceived usefulness was reported to be user or context dependent [24,31–33,36,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
In Results on interpretation (page unavailable), interviewed medical experts found the dashboard useful and preferred simple plots because they must act quickly. Limitations and outlook (page unavailable) says the plots may lack context needed for effective clinical use. These passages support the relevance of clinical context, but do not report that perceived usefulness varied across users or contexts. (LLM, conf=0.78)
Experimental — accuracy not yet measuredEight studies linked perceived usefulness to the tool's impact on workload.
“Eight studies linked perceived usefulness to the tool’s impact on workload [26,29–31,33,35,37,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
In ‘Results on interpretation’ (page not available), medical experts describe the dashboard’s plots as useful for understanding predictions at a glance and say they prefer simple plots because they must act quickly. This connects usefulness to time-pressured work, but the paper does not report an impact on workload. As one of eight cited studies, it also cannot establish the count on its own. (LLM, conf=0.83)
Experimental — accuracy not yet measuredSimple, interpretable visualizations, such as bar charts and line graphs, were preferred for understanding predictions at a glance.
“Simple, interpretable visualizations, such as bar charts and line graphs, were preferred for understanding predictions at a glance.”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
In Results on interpretation (page unavailable), interviewed medical experts said the plots helped them understand predictions at a glance and that they preferred simple plots because they had to act quickly. The passage does not say they specifically preferred bar charts or line graphs. (LLM, conf=0.85)
Experimental — accuracy not yet measuredOf the four studies that implemented multiple explanation techniques, only one directly examined participants' perceptions of the different methods.
“Of the four studies that implemented multiple explanation techniques within their models [24,32,34,38], only one directly examined participants’ perceptions of the different methods [34].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
The 'Interpretations of the DNN model' section describes four distinct interpretation plots, and 'Results on interpretation' reports interviews with medical experts about the plots’ usefulness and their preference for simple plots. The copy does not establish that the interviews directly compared participants’ perceptions of the different explanation methods, or that this was the only one of the four cited studies to do so. Page numbers were not available in the indexed passages. (LLM, conf=0.83)
Experimental — accuracy not yet measuredFour studies used more than one explanation technique in their model.
“Four studies used more than one explanation technique in their model [24,32,34,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
Zilker et al. and Schwartz et al. reported that understanding the prediction was important for trust.
“Both Zilker et al. [38] and Schwartz et al. [36] reported that understanding the prediction was important for trust”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
Validation of the model output was a major reason for explainability, reported in nine studies.
“Validation of the model output was a major reason for explainability, and was reported in nine studies [25–27,30,32,33,35,36,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
ML models in healthcare have become increasingly complex.
“Following trends in the broader ML field, ML models in healthcare have become increasingly complex [9, 10].”
From Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions (2026) · cites this paper as [10]
Five studies did not provide sufficient detail on their qualitative data analysis process, making it difficult to determine the validity of the findings.
“Five studies did not provide sufficient detail on their qualitative data analysis process, and this made it difficult to determine the validity of the findings [31,32,34,37,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
The Evaluation and application of PatWay-Net section describes structured clinician interviews, and Results on interpretation summarizes their responses (page unavailable). Neither passage assesses whether the qualitative analysis was reported in sufficient detail or whether the findings' validity is difficult to determine. Searches for qualitative analysis, interview methods, and validity did not find a passage addressing that appraisal or the count of five studies. (LLM, conf=0.74)
Experimental — accuracy not yet measuredSeveral studies lacked detail on the recruitment of participants.
“Several studies lacked detail on the recruitment of participants [32,34,37,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
The Use case description (page unavailable) describes a publicly available dataset of patient pathways from one Dutch hospital, not a review of how several studies recruited participants. The Limitations and outlook section (page unavailable) discusses sample size and generalizability, but does not assess recruitment reporting. Searches for “recruitment of participants” and patient selection found no passage supporting the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredIn Zilker et al.'s study, participants said the SHAP plots were confusing and not self-explaining because there were too many dots in the plot.
“participants said the SHAP plots were “confusing and not really self-explaining” because there are “too many dots in the plot”.”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [38]
The Results on interpretation section says medical experts positively assessed PatWay-Net’s interpretation plots, but it does not report their reactions to SHAP plots. Searches for the quoted phrases and for SHAP-related interview feedback found no passage saying participants called SHAP plots confusing or complained about too many dots. The favorable assessment of different plots does not contradict the claim about SHAP. (LLM, conf=0.75)
Experimental — accuracy not yet measuredOther predictive targets such as sepsis onset or readiness for discharge have unique labeling processes that can present additional challenges.
“other predictive targets such as sepsis onset or readiness for discharge have unique labeling processes that can present additional challenges [10].”
From Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions (2026) · cites this paper as [10]
The Introduction describes predicting ICU admission for patients with sepsis symptoms, and Clinical relevance > The case of sepsis describes sepsis as complex and time-sensitive. Neither passage addresses labeling sepsis onset or readiness for discharge, or says that those targets’ labeling processes pose additional challenges. Searches for those targets and for labeling found no passage supporting the attributed point; page numbers were not available in our copy. (LLM, conf=0.73)
Experimental — accuracy not yet measured
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- Authors
- Zilker, S., Weinzierl, S., Kraus, M., Zschech, P., & Matzner, M.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- University of Regensburg (DE)
- Leipzig University (DE)
- Corresponding author
- Zilker, Sandra
- Venue
- Health Care Management Science
- Published
- 2024-05-21
- Volume
- 27
- Issue
- 2
- Pages
- 136-167
- ISSN
- 1386-9620 (ISSN-L)
- 1572-9389
- PMC
- PMC11258202
- OpenAlex
- W4398167886
- Cited by
- 29
- References
- 73
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 4
- FWCI
- 4.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Health Care Management Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Econstor (Econstor) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- OPUS FAU (Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV), on behalf of the Universitätsbibliothek Erlangen-Nürnberg) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Friedrich-Alexander-Universität Erlangen-Nürnberg
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- sepsis, ICU admission prediction, care pathways, model interpretability, recurrent neural networks, multilayer perceptron, clinical decision support, black-box models
- MeSH terms
- Machine Learning, Humans, Intensive Care Units, Neural Networks, Computer, Sepsis, Critical Pathways
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