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Trustworthy personalized treatment selection: causal effect-trees and calibration in perioperative medicine
2026-03-04
health informatics
Title + abstract only
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BackgroundPersonalized medicine promises to tailor treatments to the individual, but it carries a hidden risk: mistaking statistical noise for actionable clinical insight. Current machine learning approaches often provide predictions, but fail to inform clinicians when those predictions are unreliable. ObjectiveDevelop a deployment-readiness framework that integrates causal inference, interpretable effect-trees, and calibration assessment to distinguish actionable signal from unreliable variati...
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