Article

A hierarchical clinical fusion transformer model for personalized opioid treatment: Development and validation in diabetic surgical patients

2026-06-08

Abstract excerpt

<h4>Background</h4> Machine learning (ML) models are increasingly used to predict adverse outcomes after surgery. However, most rely on static patient characteristics (e.g., age, comorbidities) and overlook clinician-controlled treatment decisions that can be actively modified at the point of care. Discharge opioid prescribing is a key modifiable, clinician-controlled decision, yet optimizing prescribing choices...

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Literature Corpus work: 641c16f2-3c79-5132-bff6-354082f5d187
DOI: 10.64898/2026.06.04.26353331

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