Article

Trustworthy personalized treatment selection: causal effect-trees and calibration in perioperative medicine

2026-03-04

Abstract excerpt

<h4>Background</h4> Personalized 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. <h4>Objective</h4> Develop a deployment-readiness framework that integrates causal inference, interpr...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 87940242-d17f-5595-bdba-9c541774e0c8
DOI: 10.64898/2026.03.03.26347440

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Trustworthy personalized treatment selection: causal effect-trees and calibration in perioperative medicineDOI 10.64898/2026.03.03.26347440Read

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

Deployment-readiness audit of calibration, clinical utility, and fairness in perioperative infection predictionSemantic proximity Read

Improving Predictive Models With Causal Methods – Study ProtocolSemantic proximity Read

Causal Forests versus Inverse Probability of Treatment Weighting to adjust for Cluster-Level Confounding: A Parametric and Plasmode Simulation Study based on US Hosptial Electronic Health Record DataSemantic proximity Read

Clinical Prediction Models: Foundational ConceptsSemantic proximity Read

CausalDRIFT: Causal Dimensionality Reduction via Inference of Feature Treatments for Robust Healthcare Machine LearningSemantic proximity Read

Performance metrics for models designed to predict treatment effectSemantic proximity Read

A Simulation-Based Validation Framework for Uncertainty-Aware Clinical Triage: Conformal Prediction and Ensemble Learning Applied to a Synthetic ICU Benchmark CohortSemantic proximity Read

Virtual Trials: Causally-validated treatment effects efficiently learned from an observational cancer registrySemantic proximity Read

ALEX: Automatic Language Explanations for Interpreting Treatment Effects via Multi-AgentsSemantic proximity Read

Considerations in the Reliability and Fairness Audits of Predictive Models for Advance Care PlanningSemantic proximity Read

Development and validation of interpretable Bayes machine learning model for risk adjustment of general outcome indicators: An example from UrologySemantic proximity Read