ArticleArXiv2026
A Joint Survival Modeling and Therapy Knowledge Graph Framework to Characterize Opioid Use Disorder Trajectories.
Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
motivationOpioid use disorder (OUD) often emerges following prescription opioid exposure and follows a dynamic clinical course characterized by onset, remission, and relapse. Large-scale cohorts linking electronic health records (EHR) with rich longitudinal survey data, such as the All of Us Research Program, enable computational modeling of stage-specific risk and provide an opportunity to connect predictive factors with potential treatment options.
resultsWe developed a multi-stage computational framework to model transitions across OUD onset, treatment-related remission, and relapse, and further link risk factors to treatment and intervention. Using linked EHR and survey data from All of Us, we constructed three time-to-event phenotypes: (i) OUD onset, defined from opioid prescriptions and diagnostic codes; (ii) transition to remission; and (iii) transition to relapse following remission. 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 occurrence. We fit regularized survival models for each transition and aggregated feature selection frequencies and hazard ratios to identify a compact set of high-confidence predictors. Across all stages, we observed consistent contributions from pain, mental health, and polysubstance use. Chronic pain syndromes, tobacco and nicotine dependence, anxiety and depressive disorders, and cannabis dependence were frequent and prominent predictors of OUD onset and relapse, whereas tobacco dependence during remission and other remission-coded conditions were strongly associated with the transition to remission. To support computational prioritization of therapeutic strategies, we constructed a therapy knowledge graph integrating genetic targets, biological pathways, and published evidence to link identified risk factors with candidate treatments reported in recent OUD studies and clinical guidelines.
Indexed as
Identifiers
41647240PMC12869392What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.