ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013
Using natural language processing on the free text of clinical documents to screen for evidence of homelessness among US veterans.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline 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
- Social Determinants of Health and 1-Year Buprenorphine Initiation Among Justice-Involved Veterans.JAMA network open · 2026Article
- Improving social determinants of health documentation in French electronic health records using large language models.Scientific reports · 2025Article
- Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.JCO clinical cancer informatics · 2024Article
- Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.Journal of biomedical informatics · 2024Article
- Large language models to identify social determinants of health in electronic health records.NPJ digital medicine · 2024Article
- The ENACT network is acting on housing instability and the unhoused using the open health natural language processing toolkit.Journal of clinical and translational science · 2024Article
- Using electronic health record metadata to predict housing instability amongst veterans.Preventive medicine reports · 2024Article
- Social Determinants of Health Documentation in Structured and Unstructured Clinical Data of Patients With Diabetes: Comparative Analysis.JMIR medical informatics · 2023Article
- Easy to use and validated predictive models to identify beneficiaries experiencing homelessness in Medicaid administrative data.Health services research · 2023Article
- Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities.Yearbook of medical informatics · 2023Article
- Automated identification of eviction status from electronic health record notes.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Natural language processing-driven state machines to extract social factors from unstructured clinical documentation.JAMIA open · 2023Article
- Understanding Why Many People Experiencing Homelessness Reported Migrating to a Small Canadian City: Machine Learning Approach With Augmented Data.JMIR formative research · 2023Article
- Capturing Individual-level Social Determinants from Clinical Text.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Evidence of housing instability identified by addresses, clinical notes, and diagnostic codes in a real-world population with substance use disorders.Journal of clinical and translational science · 2023Article
- Assessing longitudinal housing status using Electronic Health Record data: a comparison of natural language processing, structured data, and patient-reported history.Frontiers in artificial intelligence · 2023Article
- Using natural language processing to study homelessness longitudinally with electronic health record data subject to irregular observations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Screening for Unstable Housing in a Healthcare Setting.Public health reviews · 2023Review
- Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information retrieval algorithms based on natural language processing (NLP) of the free text of medical records have been used to find documents of interest from databases. Homelessness is a high priority non-medical diagnosis that is noted in electronic medical records of Veterans in Veterans Affairs (VA) facilities. Using a human-reviewed reference standard corpus of clinical documents of Veterans with evidence of homelessness and those without, an open-source NLP tool (Automated Retrieval Console v2.0, ARC) was trained to classify documents. The best performing model based on document level work-flow performed well on a test set (Precision 94%, Recall 97%, F-Measure 96). Processing of a naïve set of 10,000 randomly selected documents from the VA using this best performing model yielded 463 documents flagged as positive, indicating a 4.7% prevalence of homelessness. Human review noted a precision of 70% for these flags resulting in an adjusted prevalence of homelessness of 3.3% which matches current VA estimates. Further refinements are underway to improve the performance. We demonstrate an effective and rapid lifecycle of using an off-the-shelf NLP tool for screening targets of interest from medical records.
Indexed as
Identifiers
24551356PMC3900197What 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.