  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.
