A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis.
Sandra ZilkerSven WeinzierlMathias KrausPatrick ZschechMartin MatznerPublished in: Health care management science (2024)
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.
Keyphrases
- healthcare
- machine learning
- case report
- intensive care unit
- public health
- emergency department
- acute kidney injury
- neural network
- type diabetes
- health information
- septic shock
- depressive symptoms
- climate change
- human health
- artificial intelligence
- transcription factor
- big data
- mechanical ventilation
- adipose tissue
- metabolic syndrome
- sleep quality
- physical activity
- acute respiratory distress syndrome
- current status