ArticleEuropean journal of epidemiology2019
Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem.
Article in European journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 5 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 5 syntheses or guidelines pooled it, 58 citations in OpenAlex.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- 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
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled 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
- A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department.Journal of psychiatric research · 2025Article
- Evaluating Machine Learning for Predicting Youth Suicidal Behavior Up to 1 Year After Contact With Mental-Health Specialty Care.Clinical psychological science : a journal of the Association for Psychological Science · 2025Article
- Article
- Detection of suicidality from medical text using privacy-preserving large language models.The British journal of psychiatry : the journal of mental science · 2024Review
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Predicting Prenatal Depression and Assessing Model Bias Using Machine Learning Models.Biological psychiatry global open science · 2024Article
- Enhancing Suicide Attempt Risk Prediction Models with Temporal Clinical Note Features.Applied clinical informatics · 2024Article
- Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs.NPJ digital medicine · 2024Article
- Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches.JMIR mental health · 2023Article
- Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review.Journal of medical Internet research · 2023Article
- An ensemble approach for healthcare application and diagnosis using natural language processing.Cognitive neurodynamics · 2022Article
- Review
- Suicide and Maternal Mortality.Current psychiatry reports · 2022Review
- Exploring Risk Factors in Suicidal Ideation and Attempt Concept Cooccurrence Networks.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Developing a Natural Language Processing tool to identify perinatal self-harm in electronic healthcare records.PloS one · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
We developed algorithms to identify pregnant women with suicidal behavior using information extracted from clinical notes by natural language processing (NLP) in electronic medical records. Using both codified data and NLP applied to unstructured clinical notes, we first screened pregnant women in Partners HealthCare for suicidal behavior. Psychiatrists manually reviewed clinical charts to identify relevant features for suicidal behavior and to obtain gold-standard labels. Using the adaptive elastic net, we developed algorithms to classify suicidal behavior. We then validated algorithms in an independent validation dataset. From 275,843 women with codes related to pregnancy or delivery, 9331 women screened positive for suicidal behavior by either codified data (N = 196) or NLP (N = 9,145). Using expert-curated features, our algorithm achieved an area under the curve of 0.83. By setting a positive predictive value comparable to that of diagnostic codes related to suicidal behavior (0.71), we obtained a sensitivity of 0.34, specificity of 0.96, and negative predictive value of 0.83. The algorithm identified 1423 pregnant women with suicidal behavior among 9331 women screened positive. Mining unstructured clinical notes using NLP resulted in a 11-fold increase in the number of pregnant women identified with suicidal behavior, as compared to solely reliance on diagnostic codes.
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.