EURO 2025 Leeds
Abstract Submission

2369. A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis

Invited abstract in session TB-38: Forecasting, prediction and optimization 2, stream Data Science meets Optimization.

Tuesday, 10:30-12:00
Room: Michael Sadler LG19

Authors (first author is the speaker)

1. Sven Weinzierl
Friedrich-Alexander-Universität Erlangen-Nürnberg
2. Sandra Zilker
Technische Hochschule Nürnberg Georg Simon Ohm
3. Mathias Kraus
University of Regensburg
4. Patrick Zschech
TU Dresden
5. Martin Matzner
Friedrich-Alexander-Universität Erlangen-Nürnberg

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

Status: accepted

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