ArticleJournal of the American Medical Informatics Association : JAMIA2014
Automated identification of patients with a diagnosis of binge eating disorder from narrative electronic health records.
Article in Journal of the American Medical Informatics Association : JAMIA, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05304104 (Building an Equitable and Accessible System of Eating Disorder Care for VA, DoD, and Underrepresented Americans With Eating Disorders), which is not on this map. Cited by 12 papers, 2 of them syntheses that pooled it.
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.
Building an Equitable and Accessible System of Eating Disorder Care for VA, DoD, and Underrepresented Americans With Eating Disorders (EASED Study)
Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Extracting social determinants of health from electronic health records using natural language processing: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.Yearbook of medical informatics · 2015Pooled it
- Electronic Health Record-Based Phenotyping for Obsessive-Compulsive Disorder: Algorithm Development and Multicenter Validation Study.JMIR mental health · 2026Article
- Optimising the use of electronic medical records for large scale research in psychiatry.Translational psychiatry · 2024Review
- Investigating machine learning and natural language processing techniques applied for detecting eating disorders: a systematic literature review.Frontiers in psychiatry · 2024Review
- Article
- Suicidality risk among adults with binge-eating disorder.Suicide & life-threatening behavior · 2021Article
- Computer-aided therapeutic diagnosis for anorexia.Biomedical engineering online · 2020Article
- Association of depression and diabetes complications and mortality: a population-based cohort study.Epidemiology and psychiatric sciences · 2020Article
- Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation.Journal of the American Medical Informatics Association : JAMIA · 2019Observational
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- Surveillance of Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
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
No grant is acknowledged in the PubMed record.
Abstract
Binge eating disorder (BED) does not have an International Classification of Diseases, 9th or 10th edition code, but is included under 'eating disorder not otherwise specified' (EDNOS). This historical cohort study identified patients with clinician-diagnosed BED from electronic health records (EHR) in the Department of Veterans Affairs between 2000 and 2011 using natural language processing (NLP) and compared their characteristics to patients identified by EDNOS diagnosis codes. NLP identified 1487 BED patients with classification accuracy of 91.8% and sensitivity of 96.2% compared to human review. After applying study inclusion criteria, 525 patients had NLP-identified BED only, 1354 had EDNOS only, and 68 had both BED and EDNOS. Patient characteristics were similar between the groups. This is the first study to use NLP as a method to identify BED patients from EHR data and will allow further epidemiological study of patients with BED in systems with adequate clinical notes.
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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.