ArticleBMC medical informatics and decision making2018
Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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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
32 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.JCO clinical cancer informatics · 2019Pooled it
- Advantages and disadvantages of artificial intelligence in the prediction and prevention of suicide.Postepy psychiatrii neurologii · 2026Review
- Incidence and Risk Factors for Suicide Attempt During Pregnancy and the Postpartum Period.The Journal of clinical psychiatry · 2025Article
- Review
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Electronic Health Record Phenotyping of Pediatric Suicide-Related Emergency Department Visits.JAMA network open · 2024Article
- Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study.Journal of medical Internet research · 2024Article
- Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing.NPJ digital medicine · 2024Article
- Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities.Life (Basel, Switzerland) · 2024Review
- Bridging information gaps in menopause status classification through natural language processing.JAMIA open · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Identifying suicide documentation in clinical notes through zero-shot learning.Health science reports · 2023Article
- Suicidal behavior across a broad range of psychiatric disorders.Molecular psychiatry · 2023Review
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Improving ascertainment of suicidal ideation and suicide attempt with natural language processing.Scientific reports · 2022Article
- Natural language processing applied to mental illness detection: a narrative review.NPJ digital medicine · 2022Review
- Identifying Suicidal Ideation and Attempt From Clinical Notes Within a Large Integrated Health Care System.The Permanente journal · 2022Article
- Antidepressant use and risk of intubation or death in hospitalized patients with COVID-19: A retrospective cohort study of clinical effectiveness.Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundWe examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening suicidal behavior among pregnant women in electronic medical records (EMRs).
methodsWomen aged 10-64 years with at least one diagnostic code related to pregnancy or delivery (N = 275,843) from Partners HealthCare were included as our "datamart." Diagnostic codes related to suicidal behavior were applied to the datamart to screen women for suicidal behavior. Among women without any diagnostic codes related to suicidal behavior (n = 273,410), 5880 women were randomly sampled, of whom 1120 had at least one mention of terms related to suicidal behavior in clinical notes. NLP was then used to process clinical notes for the 1120 women. Chart reviews were performed for subsamples of women.
resultsUsing diagnostic codes, 196 pregnant women were screened positive for suicidal behavior, among whom 149 (76%) had confirmed suicidal behavior by chart review. Using NLP among those without diagnostic codes, 486 pregnant women were screened positive for suicidal behavior, among whom 146 (30%) had confirmed suicidal behavior by chart review.
conclusionsThe use of NLP substantially improves the sensitivity of screening suicidal behavior in EMRs. However, the prevalence of confirmed suicidal behavior was lower among women who did not have diagnostic codes for suicidal behavior but screened positive by NLP. NLP should be used together with diagnostic codes for future EMR-based phenotyping studies for suicidal behavior.
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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.