Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis
We thus demonstrate the successful deployment of machine learning based NLP algorithms to ascertain important entities for outcome prediction in mental healthcare. The observed distributions highlight which sex, ethnicity and diagnostic groups had more records of violence victimisation. Further development of these algorithms could usefully capture broader experiences, such as differentiating more efficiently between witnessed, perpetrated and experienced violence and broader violence experiences like emotional abuse.
Authors
- G. Kadra-Scalzo
- Riley Botelle (ORCID: https://orcid.org/0000-0002-3052-5698)
- Lifang Li (ORCID: https://orcid.org/0000-0003-4917-5701)
- Jyoti Sanyal
- David Chandran (ORCID: https://orcid.org/0000-0002-0123-666X)
- Robert Stewart (ORCID: https://orcid.org/0000-0002-4435-6397)
- Vishal Bhavsar (ORCID: https://orcid.org/0000-0001-7519-0599)
- Aurelie Mascio (ORCID: https://orcid.org/0000-0001-7973-6560)
- Angus Roberts (ORCID: https://orcid.org/0000-0002-4570-9801)
- Ava Mason (ORCID: https://orcid.org/0000-0001-7932-3459)
- Marcus Williams
Institutions
- King's College London (GB)
- South London and Maudsley NHS Foundation Trust (GB)
- Sandwell & West Birmingham Hospitals NHS Trust (GB)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2024-09-10
- DOI
- https://doi.org/10.3389/fpsyt.2024.1181739
- Citations
- 3
- Primary Topic
- Psychopathy, Forensic Psychiatry, Sexual Offending
- Type
- article
- Field-Weighted Citation Impact
- 1.92
Funders
- South London and Maudsley NHS Foundation Trust
- King's College Hospital NHS Foundation Trust
- UK Research and Innovation
- National Institute for Health and Care Research
- Department of Health and Social Care
- King's College London
- Medical Research Council
- National Institute for Health Research Applied Research Collaboration South London