Ava Mason ^1,2* Vishal Bhavsar ^1,3 Riley Botelle ¹ David Chandran ¹ Lifang Li ¹ Aurelie Mascio ¹ Jyoti Sanyal ³ Giouliana Kadra-Scalzo ¹ Angus Roberts ¹ Marcus Williams ^3,4 Robert Stewart ^1,3

• ¹ Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England, United Kingdom
• ² Division of Psychiatry, Faculty of Brain Sciences, University College London, London, England, United Kingdom
• ³ South London and Maudsley NHS Foundation Trust, London, United Kingdom
• ⁴ Sandwell & West Birmingham Hospitals NHS Trust, West Bromwich, United Kingdom

Experiences of violence are important risk factors for worse outcome in people with mental health conditions; however, they are not routinely collected be mental health services, so their ascertainment depends on extraction from text fields with natural language processing (NLP) algorithms. Applying previously developed neural network algorithms to routine mental healthcare records, we sought to describe the distribution of recorded violence victimisation by demographic and diagnostic characteristics. We ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019. Descriptive and regression analyses were conducted to investigate variation by age, sex, ethnic group, and diagnostic category (ICD-10 F chapter sub-headings plus posttraumatic stress disorder (PTSD) as a specific condition). Patients with a mood disorder (adjusted odds ratio 1.63, 1.55-1.72), personality disorder (4.03, 3.65-4.45), schizophrenia spectrum disorder (1.84, 1.74-1.95) or PTSD (2.36, 2.08-2.69) had a significantly increased likelihood of victimisation compared to those with other mental health diagnoses. Additionally, patients from minority ethnic groups (1.10 (1.02-1.20) for Black, for Asian compared to White groups) had significantly higher likelihood of recorded violence victimisation. Males were significantly less likely to have reported recorded violence victimisation (0.44, 0.42-0.45) than females. 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.