ArticleOpen forum infectious diseases2022
Natural Language Processing and Machine Learning to Identify People Who Inject Drugs in Electronic Health Records.
Article in Open forum infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed.
- Large Language Models Accurately Identify People Who Inject Drugs From Infectious Diseases Discharge Summaries in an Australian Hospital.Drug and alcohol review · 2026Article
- Developing and Evaluating a Natural Language Processing Algorithm to Detect Insulin Pump and Continuous Glucose Monitor Use in Electronic Health Records of Patients with Type 1 Diabetes.Diabetes technology & therapeutics · 2026Article
- Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.Current addiction reports · 2026Review
- Enhancing substance use detection in clinical notes with large language models.Drug and alcohol dependence · 2025Article
- Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study.JMIR AI · 2025Article
- Speak and You Shall Predict: Evidence That Speech at Initial Cocaine Abstinence Is a Biomarker of Long-Term Drug Use Behavior.Biological psychiatry · 2025Article
- Sensitivity and Specificity of Natural Language Processing Systems for Identification of Hospitalized People Who Use Drugs.Open forum infectious diseases · 2025Article
- The Clinical and Molecular Characteristics of Community-AssociatedOpen forum infectious diseases · 2025Article
- Enhancing Substance Use Detection in Clinical Notes with Large Language Models.Research square · 2025Article
- Comparative Effectiveness of Long-Acting Lipoglycopeptides vs Standard-of-Care Antibiotics in Serious Bacterial Infections.JAMA network open · 2025Article
- Change of Heart: Can Artificial Intelligence Transform Infective Endocarditis Management?Pathogens (Basel, Switzerland) · 2025Review
- Phenotyping people with a history of injecting drug use within electronic medical records using an interactive machine learning approach.NPJ digital medicine · 2024Article
- Perspectives on benefits and risks of creation of an "injection drug use" billing code.Journal of substance use and addiction treatment · 2024Article
- Question-answering system extracts information on injection drug use from clinical notes.Communications medicine · 2024Article
- Impact of Potential Case Misclassification by Administrative Diagnostic Codes on Outcome Assessment of Observational Study for People Who Inject Drugs.Open forum infectious diseases · 2024Article
- Hepatitis C Treatment Among Primary Care and Specialty Providers: A Single Center Study, 2015 to 2022.Journal of primary care & community healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Background: Improving the identification of people who inject drugs (PWID) in electronic medical records can improve clinical decision making, risk assessment and mitigation, and health service research. Identification of PWID currently consists of heterogeneous, nonspecific Methods: We manually reviewed 1000 records of patients diagnosed with Results: Random forest with and without NegEx were the best-performing NLP/ML algorithms in the training set. Random forest with NegEx outperformed all Conclusions: NLP/ML outperformed
Indexed as
Identifiers
What 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.