[论文解读] A Joint Survival Modeling and Therapy Knowledge Graph Framework to Characterize Opioid Use Disorder Trajectories

Mengman Wei, Stanislav Listopad+1 more|arXiv (Cornell University)|Jan 19, 2026

Opioid Use Disorder Treatment被引用 0

一句话总结

该论文提出一个多阶段生存建模框架，用于OUD轨迹（发病、缓解、复发）的研究，基于All of Us数据，并将风险因素与治疗知识图谱关联，以支持治疗优先级的确定。

ABSTRACT

Motivation: Opioid use disorder (OUD) often arises after prescription opioid exposure and follows transitions among onset, remission, and relapse. Linked EHR-survey resources such as the All of Us Research Program enable stage-specific risk modeling and connection to intervention options. Results: We built a multi-stage framework to model time-to-onset, time-to-remission, and time-to-relapse after remission using All of Us EHR and survey data. For each participant we derived longitudinal predictors from clinical conditions and survey concepts, including recent (1/3/12-month) event counts, cumulative exposures, and time since last event. We fit regularized Cox models for each transition and aggregated selection frequencies and hazard ratios to identify a compact set of high-confidence predictors. Pain, mental health, and polysubstance use contributed across stages: chronic pain syndromes, tobacco/nicotine dependence, anxiety and depressive disorders, and cannabis dependence prominently predicted onset and relapse, whereas tobacco dependence during remission and other remission-coded conditions were strongly associated with transition to remission. To support therapeutic prioritization, we constructed a therapy knowledge graph integrating genetic targets, biological pathways, and published evidence to map identified risk factors to candidate treatments in recent OUD studies and clinical guidelines.

研究动机与目标

• 建模三种临床意义重大的OUD转变：发病、缓解和复发。
• 从高维EHR与调查数据中识别紧凑、具有高置信度的预测变量。
• 将识别出的风险因素通过知识图谱与循证治疗相连，以支持决策。

提出的方法

• 从链接的EHR与调查数据中构建OUD发病、缓解和复发的三种时间到事件结果。
• 在痛苦、心理健康和多药使用概念上设计时间索引特征（最近计数、累积计数、距上次的天数）。
• 对每个转变采用L1正则化的Cox模型（Lasso），使用开始–停止形式与交叉验证调参。
• 在数据分区中聚合特征选择频次和风险比，以识别稳健的预测因子。
• 构建整合基因、通路和药物的治疗知识图谱，将风险因素与MOUD及其他治疗连接起来。
• 在图上使用个性化PageRank方法对候选药物进行排序，并提供可解释的输出。

Figure 1: Predictors of OUD onset. Hazard ratios (HRs) from time-to-event models for OUD onset. HR $>1$ indicates increased hazard (earlier onset) and HR $<1$ indicates decreased hazard. Predictors shown are the 24 prioritized features selected by the modeling/feature-selection pipeline; Selected in

实验结果

研究问题

• RQ1 在一个规模大、人群多样的队列中，OUD发病、缓解和复发各阶段的风险因素是什么？
• RQ2 来自高维EHR与调查数据的时变预测变量是否可以被浓缩为每个OUD转变的紧凑、可解释的风险因素集？
• RQ3 如何利用知识图谱将识别出的风险因素与循证治疗连接起来，并为治疗选项的优先级排序提供依据？

主要发现

• 疼痛相关病症、烟草/尼古丁使用以及精神健康疾病是OUD发病的显著预测因素。
• 缓解与MAT/MOUD暴露以及近期临床参与密切相关，其他物质使用及焦虑相关预测因素同样重要。
• 复发风险与近期临床活动和多药物使用指标相关，一些极端风险比可能反映稀疏数据或复杂性。
• 知识图谱优先考虑已确立的MOUD药物，并为非MAT候选药物识别出机制性聚簇，提供疾病机制层面的治疗假设生成。

Figure 2: Top predictors of OUD remission. Hazard ratios (HRs) summarize associations with time to OUD remission; HR $>1$ indicates a higher hazard of remission (i.e., faster remission), and HR $<1$ indicates a lower hazard. To improve readability, the x-axis is shown on a log scale; values above 10