ArticleJournal of the American Medical Informatics Association : JAMIA2013
Validating a strategy for psychosocial phenotyping using a large corpus of clinical text.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Using electronic health record metadata to predict housing instability amongst veterans.Preventive medicine reports · 2024Article
- Article
- Bootstrapping semi-supervised annotation method for potential suicidal messages.Internet interventions · 2022Review
- Psychosocial phenotyping as a personalization strategy for chronic disease self-management interventions.American journal of translational research · 2021Article
- Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder.BMC medical informatics and decision making · 2020Article
- Predicting 72-hour and 9-day return to the emergency department using machine learning.JAMIA open · 2019Article
- CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support.Applied clinical informatics · 2019Article
- Associations of unconventional natural gas development with depression symptoms and disordered sleep in Pennsylvania.Scientific reports · 2018Article
- Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Clinical information extraction applications: A literature review.Journal of biomedical informatics · 2018Review
- Empirical advances with text mining of electronic health records.BMC medical informatics and decision making · 2017Article
- 'Big data' in mental health research: current status and emerging possibilities.Social psychiatry and psychiatric epidemiology · 2016Review
- v3NLP Framework: Tools to Build Applications for Extracting Concepts from Clinical Text.EGEMS (Washington, DC) · 2016Article
- Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.Yearbook of medical informatics · 2015Review
- Big data in medicine is driving big changes.Yearbook of medical informatics · 2014Review
- Extracting Concepts Related to Homelessness from the Free Text of VA Electronic Medical Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Using natural language processing on the free text of clinical documents to screen for evidence of homelessness among US veterans.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2013Article
- CQL4NLP: Development and Integration of FHIR NLP Extensions in Clinical Quality Language for EHR-driven Phenotyping.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop algorithms to improve efficiency of patient phenotyping using natural language processing (NLP) on text data. Of a large number of note titles available in our database, we sought to determine those with highest yield and precision for psychosocial concepts. MATERIALS AND
methodsFrom a database of over 1 billion documents from US Department of Veterans Affairs medical facilities, a random sample of 1500 documents from each of 218 enterprise note titles were chosen. Psychosocial concepts were extracted using a UIMA-AS-based NLP pipeline (v3NLP), using a lexicon of relevant concepts with negation and template format annotators. Human reviewers evaluated a subset of documents for false positives and sensitivity. High-yield documents were identified by hit rate and precision. Reasons for false positivity were characterized.
resultsA total of 58 707 psychosocial concepts were identified from 316 355 documents for an overall hit rate of 0.2 concepts per document (median 0.1, range 1.6-0). Of 6031 concepts reviewed from a high-yield set of note titles, the overall precision for all concept categories was 80%, with variability among note titles and concept categories. Reasons for false positivity included templating, negation, context, and alternate meaning of words. The sensitivity of the NLP system was noted to be 49% (95% CI 43% to 55%).
conclusionsPhenotyping using NLP need not involve the entire document corpus. Our methods offer a generalizable strategy for scaling NLP pipelines to large free text corpora with complex linguistic annotations in attempts to identify patients of a certain phenotype.
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