ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Applications of Aspect-based Sentiment Analysis on Psychiatric Clinical Notes to Study Suicide in Youth.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Suicide Risk in 8- to 12-Year-Old Children: Retrospective Review of Emergency Department Electronic Health Records.JAACAP open · 2026Article
- Considerations and Challenges When Using Clinical and Vital Record Review for Suicide Research.Journal of patient safety · 2025Article
- Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry.Frontiers in psychiatry · 2024Review
- Building a Natural Language Processing Artificial Intelligence to Predict Suicide-Related Events Based on Patient Portal Message Data.Mayo Clinic proceedings. Digital health · 2023Article
- Statistical and artificial intelligence techniques to identify risk factors for suicide in children and adolescents.Science progressObservational
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Understanding and identifying the risk factors associated with suicide in youth experiencing mental health concerns is paramount to early intervention. 45% of patients are admitted annually for suicidality at BC Children's Hospital. Natural Language Processing (NLP) approaches have been applied with moderate success to psychiatric clinical notes to predict suicidality. Our objective was to explore whether machine-learning-based sentiment analysis could be informative in such a prediction task. We developed a psychiatry-relevant lexicon and identified specific categories of words, such as thought content and thought process that had significantly different polarity between suicidal and non-suicidal cases. In addition, we demonstrated that the individual words with their associated polarity can be used as features in classification models and carry informative content to differentiate between suicidal and non-suicidal cases. In conclusion, our study reveals that there is much value in applying NLP to psychiatric clinical notes and suicidal prediction.
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Identifiers
34457137PMC8378644What OpenQuestion holds
Registered trials
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