Background The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim’s and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes. Objective The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. Methods We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. Results In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%). Conclusions A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.

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BACKGROUND The NSW Police Force records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim’s and person of interest’s (POI) mental health status could be useful in the better management of DV events and improve health, justice and social outcomes. OBJECTIVE To present the prevalence of extracted mental illnesses mentions for POIs and victims in police recorded DV events. METHODS We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police recorded DV events. RESULTS In 416,441 police recorded DV events with single POIs and single victims, we identified 64,587 events (15.5%) with at least one mental illness mention versus 4,295 (1.0%) recorded in the structured fixed fields. Two thirds (66.8%; 67,582) of mental illnesses were associated with POIs versus 20.7% (18,298) with victims with depression the most common in both victims (22.4%; 2,822) and POIs (19.0%; 7,496). Mental illnesses were most common among POIs aged 0–14 years old (38.4%) and for victims over 65 years old (5.4%). CONCLUSIONS A wealth of mental illness information exists within police recorded DV events that can be extracted using text mining. The results showed a large prevalence of mood-related illnesses for both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.