Karystianis George, Adily Armita, Schofield Peter, Knight Lee, Galdon Clara, Greenberg David, Jorm Louisa, Nenadic Goran, Butler Tony
Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Neuropsychiatry Service, Hunter New England Health, Newcastle, Australia.
J Med Internet Res. 2018 Sep 13;20(9):e11548. doi: 10.2196/11548.
Vast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes.
In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text.
We used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims.
The precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.25%, 3269) and POIs (18.70%, 8944), followed by alcohol abuse for POIs (12.19%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.66%, 1714).
The results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.
新南威尔士州警方每年处理大量家庭暴力（DV）事件，并将事件详情、受害者及相关人员（POI）的信息作为结构化定量数据和非结构化自由文本记录在WebCOPS（计算机化行动警务系统的基于网络的界面）数据库中。虽然结构化数据用于报告目的，但自由文本尚未用于家庭暴力报告和监测目的。
在本文中，我们探讨文本挖掘能否从这些非结构化文本中自动识别心理健康障碍。
我们使用一组200个记录在案的家庭暴力事件训练集，设计了一种基于文本中词汇模式的知识驱动方法，以提示相关人员和受害者存在心理健康障碍。
在一组100个家庭暴力事件的评估集中，与相关人员和受害者心理健康障碍相关的精确率分别为97.5%和87.1%。将我们的方法应用于近50万个家庭暴力事件的大规模语料库后，我们识别出77995个提及心理健康障碍的事件（15.83%），其中76.96%（60032/77995）与相关人员有关，16.47%（12852/77995）与受害者有关，6.55%（5111/77995）与两者都有关。抑郁症是受害者（22.25%，3269例）和相关人员（18.70%，8944例）中提及最多的心理健康障碍，其次是相关人员中的酒精滥用（12.19%，5829例）和受害者中的各种焦虑症（如惊恐障碍、广泛性焦虑症）（11.66%，1714例）。
结果表明，文本挖掘可以从警方记录的家庭暴力事件中自动提取目标信息，以支持进一步开展关于心理健康障碍与家庭暴力之间联系的公共卫生研究。
