J Am Med Inform Assoc. 2025 Jul 23:ocaf121. doi: 10.1093/jamia/ocaf121. Online ahead of print.
ABSTRACT
OBJECTIVE: Risk prediction models are used in hospitals to identify pediatric patients at risk of clinical deterioration, enabling timely interventions and rescue. The objective of this study was to develop a new explainer algorithm that uses a patient’s clinical notes to generate text-based explanations for risk prediction alerts.
MATERIALS AND METHODS: We conducted a retrospective study of 39 406 patient admissions to the American Family Children’s Hospital at the University of Wisconsin-Madison (2009-2020). The pediatric Calculated Assessment of Risk and Triage (pCART) validated risk prediction model was used to identify children at risk for deterioration. A transformer model was trained to use clinical notes from the 12-hour period preceding each pCART score to predict whether a patient was flagged as at risk. Then, label-aware attention highlighted text phrases most important to an at-risk alert. The study cohort was randomly split into derivation (60%) and validation (20%) data, and a separate test (20%) was used to evaluate the explainer’s performance.
RESULTS: Our pCART Explainer algorithm performed well in discriminating at-risk pCART alert vs no alert (c-statistic 0.805). Sample explanations from pCART Explainer revealed clinically important phrases such as “rapid breathing,” “fall risk,” “distension,” and “grunting,” thereby demonstrating excellent face validity.
DISCUSSION: The pCART Explainer could quickly orient clinicians to the patient’s condition by drawing attention to key phrases in notes, potentially enhancing situational awareness and guiding decision-making.
CONCLUSION: We developed pCART Explainer, a novel algorithm that highlights text within clinical notes to provide medically relevant context about deterioration alerts, thereby improving the explainability of the pCART model.
PMID:40700686 | DOI:10.1093/jamia/ocaf121
