Journal article
Detection of Self-Harm in Electronic Mental Health Records Using Privacy-Preserving Local Language Models: Methodological Study
- Abstract:
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BackgroundSelf-harm is the strongest risk factor for suicide and an important outcome for mental health care. Although prevalent in clinical populations, it is often imprecisely captured in routinely collected clinical data, where it is often recorded and stored as unstructured free text. Contemporary language models, such as GPT (OpenAI) and Gemini (Google), can analyze free-text clinical notes, but such models may violate data governance of processing sensitive patient data.ObjectiveThis st...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 337.8KB, Terms of use)
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- Publisher copy:
- 10.2196/87586
Authors
- Publisher:
- JMIR Publications
- Journal:
- JMIR Mental Health More from this journal
- Volume:
- 13
- Pages:
- e87586
- Article number:
- v13i2e87586
- Publication date:
- 2026-06-02
- Acceptance date:
- 2026-03-09
- DOI:
- EISSN:
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2368-7959
- ISSN:
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2368-7959
- Pmid:
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42227874
- Language:
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English
- Keywords:
- Source identifiers:
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4219232
- Deposit date:
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2026-06-11
- ARK identifier:
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- Copyright date:
- 2026
- Licence:
- CC Attribution (CC BY)
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