Patient-Reported Survey Data Improve Prediction of Opioid Use Disorder

2026-09-10

AI Summary

研究通过结合患者报告的调查数据与电子健康记录，利用多种机器学习模型提高了阿片类药物使用障碍的预测准确性。

Abstract

Electronic health records (EHRs) may incompletely capture patient-reported factors associated with opioid use disorder (OUD). We evaluated whether survey data improve prediction of a first recorded OUD diagnosis among 267,747 All of Us participants with documented opioid exposure, including 15,287 OUD cases. We compared EHR-only and EHR+survey models across 6-, 12-, and 24-month look-back windows using logistic regression, random forest, XGBoost, LightGBM, multilayer perceptron, LSTM, GRU, and Transformer. Survey augmentation improved PR-AUC across all 24 model-window combinations by 0.0087-0.0505; the best 24-month LightGBM model improved from 0.6219 to 0.6603. Survey coverage increased with longer windows and differed by OUD status (24 months: 21.7% OUD-positive vs. 60.7% OUD-negative). Permutation analysis ranked survey features as the second most important information domain at 24 months in both evaluated models. Patient-reported data provide complementary predictive signals beyond structured EHRs while highlighting the importance of survey availability.

Problem

• Opioid Use Disorder
• Electronic Health Records
• Patient-Reported Factors

Innovation

• survey data
• prediction improvement
• Opioid Use Disorder (OUD)
• machine learning models
• electronic health records (EHR)