ArticlePloS one2023
Computational phenotypes for patients with opioid-related disorders presenting to the emergency department.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.PLOS digital health · 2026Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project.medRxiv : the preprint server for health sciences · 2025Article
- Factors associated with emergency room visits among patients with opioid use disorder: A study of buprenorphine-treated and untreated patients.Drug and alcohol dependence reports · 2025Article
- Article
- Correction: Computational phenotypes for patients with opioid-related disorders presenting to the emergency department.PloS one · 2025Article
- Classifying opioid use disorder based on diagnostic criteria items using cluster analysis.Industrial psychiatry journalArticle
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
objectiveWe aimed to discover computationally-derived phenotypes of opioid-related patient presentations to the ED via clinical notes and structured electronic health record (EHR) data.
methodsThis was a retrospective study of ED visits from 2013-2020 across ten sites within a regional healthcare network. We derived phenotypes from visits for patients ≥18 years of age with at least one prior or current documentation of an opioid-related diagnosis. Natural language processing was used to extract clinical entities from notes, which were combined with structured data within the EHR to create a set of features. We performed latent dirichlet allocation to identify topics within these features. Groups of patient presentations with similar attributes were identified by cluster analysis.
resultsIn total 82,577 ED visits met inclusion criteria. The 30 topics were discovered ranging from those related to substance use disorder, chronic conditions, mental health, and medical management. Clustering on these topics identified nine unique cohorts with one-year survivals ranging from 84.2-96.8%, rates of one-year ED returns from 9-34%, rates of one-year opioid event 10-17%, rates of medications for opioid use disorder from 17-43%, and a median Carlson comorbidity index of 2-8. Two cohorts of phenotypes were identified related to chronic substance use disorder, or acute overdose.
conclusionsOur results indicate distinct phenotypic clusters with varying patient-oriented outcomes which provide future targets better allocation of resources and therapeutics. This highlights the heterogeneity of the overall population, and the need to develop targeted interventions for each population.
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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.