用因果机器学习预测手术中患者阿片类药物剂量，减少副作用风险。
Causal Machine Learning for Patient-Level Intraoperative Opioid Dose Prediction from Electronic Health Records
- 基于电子病历数据，用因果模型分析药物与疼痛/副作用的关系。
- 可个性化推荐剂量，兼顾镇痛效果与降低不良反应。
- 适合临床医生优化术中镇痛管理，提升患者安全。
本文提出OPIAID算法，一种用于预测和推荐个体患者术中阿片类药物剂量的新方法。该算法利用观察性电子健康记录（EHR）数据训练机器学习模型，通过因果机器学习方法，理解阿片类药物剂量、患者特异性特征及术中变量与疼痛控制和阿片类相关不良事件（ORADE）之间的关系。算法考虑患者个体特征及不同阿片类药物的影响，实现个性化剂量推荐。本文阐述了算法的方法论与架构，讨论了关键假设，并提出了性能评估策略。
原文摘要 · Abstract (English)
This paper introduces the OPIAID algorithm, a novel approach for predicting and recommending personalized opioid dosages for individual patients. The algorithm optimizes pain management while minimizing opioid related adverse events (ORADE) by employing machine learning models trained on observational electronic health records (EHR) data. It leverages a causal machine learning approach to understand the relationship between opioid dose, case specific patient and intraoperative characteristics, and pain versus ORADE outcomes. The OPIAID algorithm considers patient-specific characteristics and the influence of different opiates, enabling personalized dose recommendations. This paper outlines the algorithm's methodology and architecture, and discusses key assumptions, and approaches to evaluating its performance.
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