ArticleScientific reports2021
A natural language processing approach for identifying temporal disease onset information from mental healthcare text.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Two-Stage Extraction of Clinical Course Information From Psychiatric Discharge Summaries Using Fine-Tuned Large Language Models: Cross-Validation Study.JMIR formative research · 2026Article
- Characterization and validation of electronic medical record data for pharmacoepidemiologic research.Frontiers in pharmacology · 2026Article
- From predictive algorithms to generative intelligence: a decadal bibliometric mapping of global research frontiers in AI-driven mental health (2016-2025).Australian journal of psychology · 2026Review
- Identifying psychosis episodes in psychiatric admission notes via rule-based methods, machine learning, and pre-trained language models.Translational psychiatry · 2025Article
- Identification of Child Survivors of Sex Trafficking From Electronic Health Records: An Artificial Intelligence Guided Approach.Child maltreatment · 2024Article
- Scalable incident detection via natural language processing and probabilistic language models.Scientific reports · 2024Article
- Development of a Corpus Annotated With Mentions of Pain in Mental Health Records: Natural Language Processing Approach.JMIR formative research · 2023Article
- A Systematic Temporal Extraction Pipeline for Medical Concepts in Clinical Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions.Wireless personal communications · 2023Review
- Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability Program.Psychiatric services (Washington, D.C.) · 2023Article
- Design considerations for a hierarchical semantic compositional framework for medical natural language understanding.PloS one · 2023Article
- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- Article
- Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service.Frontiers in digital health · 2021Article
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Authors and funding
7 authors.
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Abstract
Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset, as longer delays in treatment are associated with worse intervention outcomes. The growing adoption of electronic health records (EHRs) within mental health services provides an invaluable opportunity to study this problem at scale retrospectively. However, disease onset information is often only available in open text fields, requiring natural language processing (NLP) techniques for automated analyses. Since this variable can be documented at different points during a patient's care, NLP methods that model clinical and temporal associations are needed. We address the identification of psychosis onset by: 1) manually annotating a corpus of mental health EHRs with disease onset mentions, 2) modelling the underlying NLP problem as a paragraph classification approach, and 3) combining multiple onset paragraphs at the patient level to generate a ranked list of likely disease onset dates. For 22/31 test patients (71%) the correct onset date was found among the top-3 NLP predictions. The proposed approach was also applied at scale, allowing an onset date to be estimated for 2483 patients.
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