Text mining police narratives for mentions of mental disorders in family and domestic violence events

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In this paper, we describe the feasibility of using a text-mining method to generate new insights relating to family and domestic violence (FDV) from free-text police event narratives. Despite the rich descriptive content of the event narratives regarding the context and individuals involved in FDV events, the police narratives are untapped as a source of data to generate research evidence. We used text mining to automatically identify mentions of mental disorders for both persons of interest (POIs) and victims of FDV in 492,393 police event narratives created between January 2005 and December 2016. Mentions of mental disorders for both POIs and victims were identified in nearly 15.8 percent (77,995) of all FDV events. Of all events with mentions of mental disorder, 76.9 percent (60,032) and 16.4 percent (12,852) were related to either POIs or victims, respectively. The next step will be to use actual diagnoses from NSW Health records to determine concordance between the two data sources. We will also use text mining to extract information about the context of FDV events among key at-risk groups.


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Abbe A, Grouin C, Zweigenbaum P & Falissard B 2015. Text mining applications in psychiatry: a systematic literature review. International Journal of Methods in Psychiatric Research 25: 86–100

Ananiadou S, Kell DB & Tsujii J 2006. Text mining and its potential applications in systems biology. Trends in Biotechnology 24(12): 571–579

Ananiadou S & Mcnaught J 2006. Text mining for biology and biomedicine. London: Artech House

Australian Bureau of Statistics 2017. Personal safety survey ABS cat. No. 4906. Canberra: Commonwealth of Australia. https://www.abs.gov.au/ausstats/abs@.nsf/mf/4906.0

Australian Government Department of Social Services 2010. National plan to reduce violence against women and their children 2010–2022. Canberra: Department of Social Services. https://www.dss.gov.au/women/programs-services/reducing-violence/the-na…

Bedi G & Goddard C 2007. Intimate partner violence: What are the impacts on children? Australian Psychologist 42: 66–77

Bundschus M, Dejori M, Stetter M, Tresp V & Kriegel HP 2008. Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics 9: 207

Chalkley R & Strang H 2017. Predicting domestic homicides and serious violence in Dorset: A replication of Thornton’s Thames Valley analysis. Cambridge Journal of Evidence-Based Policing 1: 81–92

Cunningham H, Tablan V, Robets A & Vontcheva K 2013. Getting more out of biomedical documents with GATE’s full lifecycle open source text analysis. PLoS Computational Biology: 9

Dowling C & Morgan A 2019. Predicting repeat domestic violence: Improving police risk assessment. Trends & issues in crime and criminal justice no. 581. Canberra: Australian Institute of Criminology. https://www.aic.gov.au/publications/tandi/tandi581

Farrer TJ, Frost RB & Hedges DW 2012. Prevalence of Traumatic Brain Injury in Intimate Partner Violence Offenders Compared to the General Population: A Meta-Analysis. Trauma, Violence, & Abuse 13: 77–82

Feldman R & Sanger J 2007. The text mining handbook: Advanced approaches in analyzing unstructured data. New York: Cambridge University Press

Fitzgerald J & People J 2006. Victims of abduction: Patterns and case studies. Crime and Justice 69: 1–16

Grech K & Burgess M 2011. Trends and patterns in domestic violence assault: 2001 to 2010. Crime and Justice Statistics 61: 1–14

Howard LM 2012. Domestic violence: its relevance to psychiatry. Advances in Psychiatric Treatment 18(2): 129–136

Hristovski D, Friedman C, Rindflesch TC & Peterlin B 2006. Exploiting semantic relations for literature-based discovery. AMIA Annual Symposium Proceedings 2006: 349–53

Jimeno A, Jimenez-Ruiz E, Lee V, Gaudan S, Berlanga R & Rebholz-Schuhmann D 2008. Assessment of disease named entity recognition on a corpus of annotated sentences. BMC Bioinformatics 9: S3

Kao A & Poteet SR 2007. Natural language processing and text mining. London: Springer

Macdonald W & Fitzgerald J 2014. Understanding fraud: The nature of fraud offences recorded by NSW Police. Crime and Justice Bulletin 180: 1–16

McGinn T, Taylor B, McColgan M & Lagdon 2015. Survivor Perspectives on IPV Perpetrator Interventions: A Systematic Narrative Review. Trauma, Violence, & Abuse 17: 239–55

NSW Department of Health 2016. It Stops Here. http://www.health.nsw.gov.au/kidsfamilies/protection/Pages/safer-pathwa…

NSW Government 2018. Reducing domestic violence reoffending. Department of Premier and Cabinet

NSW Police Force 2011. New computer operating system brings NSW Police Force into the 21st century. NSW Police Force Facebook notes, September 15

Nittis M, Hughes R, Gray C & Ashton M 2012. Domestic violence documentation project 2012. Journal of Forensic and Legal Medicine 20: 683–689

Oram S, Trevillion K, Feder G & Howard LM 2013. Prevalence of experiences of domestic violence among psychiatric patients: Systematic review. British Journal of Psychiatry 202: 94–99

Ringland C 2018. The Domestic Violence Safety Assessment Tool (DVSAT) and intimate partner repeat victimisation. Crime and Justice Bulletin no. 213. Sydney: NSW Bureau of Crime Statistics and Research

Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC & Chute CG 2010. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): Architecture, component evaluation and applications. Journal of the American Medical Informatics Association 17(5): 507–13

Solomon PL, Cavanaugh MM & Gelles RJ 2005. Family violence among adults with severe mental illness: A neglected area of research. Trauma, Violence, & Abuse 6(1):40–54

Spasić I, Livsey J, Keane JA & Nenadić G 2014. Text mining of cancer-related information: review of current status and future directions. International Journal of Medical Informatics 83(9): 605–623

Thornton S 2017. Police attempts to predict domestic murder and serious assaults: Is early warning possible yet? Cambridge Journal of Evidence-Based Policing 1(2–3): 64–80

Trevillion K, Oram S, Feder G & Howard LM 2012. Experiences of domestic violence and mental disorders: A systematic review and meta-analysis. PLoS One 712: 1– 12

Van Dorn R, Volavka J & Johnson N 2012. Mental disorder and violence: Is there a relationship beyond substance use? Social Psychiatry and Psychiatric Epidemiology 473: 487–503

VicHealth 2004. The health costs of violence: Measuring the burden of disease caused by intimate partner violence: https://www.vichealth.vic.gov.au/media-and-resources/publications/the-h…

World Health Organization 2017. The ICD-10 classification of mental and behavioral disorders. https://www.who.int/classifications/icd/en/bluebook.pdf