aceAn Explainable AI-Guided Framework for Cancer Pain Phenotyping and Clinical Decision-Making
Purpose Current pain management often relies primarily on pain intensity, which may not capture patient variability or guide individualized care. Advances in machine learning (ML) and explainable artificial intelligence (XAI) offer opportunities to improve assessment by identifying clinically meaningful subgroups and enhancing model interpretability. This study aimed to identify distinct cancer pain phenotypes using explainable machine learning and to develop a phenotype-guided clinical decision framework to sup…
In 260 cancer patients, explainable ML identified four pain phenotypes and showed that catastrophizing and distress predicted group membership more than pain intensity, supporting a framework for individualized care.
The study is cross-sectional, and the authors only say that adding psychological and behavioral dimensions to routine assessment 'may improve' decisions and outcomes, so benefit for patients is not demonstrated.
Evidence
- Peer-reviewedPain Management Nursing2026-10-08
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Truvace Impact Record TRV-2026-1618, v1: “An Explainable AI-Guided Framework for Cancer Pain Phenotyping and Clinical Decision-Making.” Truvace, 2026-10-16. /record/TRV-2026-1618 (accessed at citation time). sha256 b13569f50988e6a0…
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