Causal Machine Learning for Patient-Level Intraoperative Opioid Dose Prediction from Electronic Health Records

• URL: http://arxiv.org/abs/2508.09059v1
• Date: Tue, 12 Aug 2025 16:20:04 GMT
• Title: Causal Machine Learning for Patient-Level Intraoperative Opioid Dose Prediction from Electronic Health Records
• Authors: Jonas Valbjørn Andersena, Anders Peder Højer Karlsen, Markus Harboe Olsen, Nikolaj Krebs Pedersen,
• Abstract summary: OPIAID is a machine learning algorithm for predicting and recommending personalized opioid dosages for individual patients.<n>This paper outlines the algorithm's methodology and architecture, and discusses key assumptions, and approaches to evaluating its performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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