Text mining police narratives to identify types of abuse and victim injuries in family and domestic violence events

2021 ◽  
Author(s):  
Armita Adily ◽  
George Karystianis
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
Structured Data ◽  
Police Force ◽  
Free Text ◽  
Mining Method ◽  
Large Corpus

Police attend numerous family and domestic violence (FDV) related events each year and record details of these events as both structured data and unstructured free-text narratives. These descriptive narratives include information about the types of abuse (eg physical, emotional, financial) and the injuries sustained by victims. However, this information is not used in research. In this paper we demonstrate the application of an automated text mining method to identify abuse types and victim injuries in a large corpus of NSW Police Force FDV event narratives (492,393) recorded between January 2005 and December 2016. Specific types of abuse and victim injuries were identified in 71.3 percent and 35.9 percent of FDV event narratives respectively. The most commonly identified abuse types mentioned in the narratives were non-physical (55.4%). Our study supports the application of text mining for use in FDV research and monitoring.

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

2021 ◽  
Author(s):  
Armita Adily ◽  
George Karystianis
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
Mental Disorders ◽  
Research Evidence ◽  
Risk Groups ◽  
Free Text ◽  
Mining Method ◽  
Health Records ◽  
The Rich ◽  
Extract Information

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.

scholarly journals Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study (Preprint)

2020 ◽  
Author(s):  
George Karystianis ◽  
Annabeth Simpson ◽  
Armita Adily ◽  
Peter Schofield ◽  
David Greenberg ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Text Mining ◽  
New South ◽  
Mental Illnesses ◽  
Free Text ◽  
South Wales ◽  
Police Records ◽  
Health Justice ◽  
Improve Health

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.

scholarly journals Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study (Preprint)

2018 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Large Scale ◽  
New South ◽  
New South Wales ◽  
Mental Health Disorders ◽  
Free Text ◽  
South Wales ◽  
Health Disorders

BACKGROUND 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. OBJECTIVE In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. METHODS 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. RESULTS 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 (22.30%, 3258) and POIs (18.73%, 8918), followed by alcohol abuse for POIs (12.24%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.43%, 1671). CONCLUSIONS 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.

scholarly journals Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study (Preprint)

2018 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter W Schofield ◽  
David Greenberg ◽  
Louisa Jorm ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
Automated Analysis ◽  
Public Health Research ◽  
Free Text ◽  
Large Set ◽  
South Wales ◽  
Police Reports ◽  
Relationship Of ◽  
The Relationship

BACKGROUND The police attend numerous domestic violence events each year, recording details of these events as both structured (coded) data and unstructured free-text narratives. Abuse types (including physical, psychological, emotional, and financial) conducted by persons of interest (POIs) along with any injuries sustained by victims are typically recorded in long descriptive narratives. OBJECTIVE We aimed to determine if an automated text mining method could identify abuse types and any injuries sustained by domestic violence victims in narratives contained in a large police dataset from the New South Wales Police Force. METHODS We used a training set of 200 recorded domestic violence events to design a knowledge-driven approach based on syntactical patterns in the text and then applied this approach to a large set of police reports. RESULTS Testing our approach on an evaluation set of 100 domestic violence events provided precision values of 90.2% and 85.0% for abuse type and victim injuries, respectively. In a set of 492,393 domestic violence reports, we found 71.32% (351,178) of events with mentions of the abuse type(s) and more than one-third (177,117 events; 35.97%) contained victim injuries. “Emotional/verbal abuse” (33.46%; 117,488) was the most common abuse type, followed by “punching” (86,322 events; 24.58%) and “property damage” (22.27%; 78,203 events). “Bruising” was the most common form of injury sustained (51,455 events; 29.03%), with “cut/abrasion” (28.93%; 51,284 events) and “red marks/signs” (23.71%; 42,038 events) ranking second and third, respectively. CONCLUSIONS The results suggest that text mining can automatically extract information from police-recorded domestic violence events that can support further public health research into domestic violence, such as examining the relationship of abuse types with victim injuries and of gender and abuse types with risk escalation for victims of domestic violence. Potential also exists for this extracted information to be linked to information on the mental health status.

scholarly journals Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study

10.2196/23725 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e23725
Author(s):  
George Karystianis ◽  
Annabeth Simpson ◽  
Armita Adily ◽  
Peter Schofield ◽  
David Greenberg ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Text Mining ◽  
New South ◽  
Mental Illnesses ◽  
Free Text ◽  
South Wales ◽  
Police Records ◽  
Health Justice ◽  
Improve Health

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.

Secure Secondary Use of Clinical Data with Cloud-based NLP Services

2015 ◽  
Vol 54 (03) ◽  
pp. 276-282 ◽  
Author(s):  
J. Christoph ◽  
L. Griebel ◽  
I. Leb ◽  
I. Engel ◽  
F. Köpcke ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Text Mining ◽  
Clinical Data ◽  
Structured Data ◽  
Use Cases ◽  
University Hospital ◽  
Free Text ◽  
Secondary Use ◽  
Computing Paradigm ◽  
Multiple Data

SummaryObjectives: The secondary use of clinical data provides large opportunities for clinical and translational research as well as quality assurance projects. For such purposes, it is necessary to provide a flexible and scalable infrastructure that is compliant with privacy requirements. The major goals of the cloud4health project are to define such an architecture, to implement a technical prototype that fulfills these requirements and to evaluate it with three use cases.Methods: The architecture provides components for multiple data provider sites such as hospitals to extract free text as well as structured data from local sources and de-identify such data for further anonymous or pseudonymous processing. Free text documentation is analyzed and transformed into structured information by text-mining services, which are provided within a cloud-computing environment. Thus, newly gained annotations can be integrated along with the already available structured data items and the resulting data sets can be uploaded to a central study portal for further analysis.Results: Based on the architecture design, a prototype has been implemented and is under evaluation in three clinical use cases. Data from several hundred patients provided by a University Hospital and a private hospital chain have already been processed.Conclusions: Cloud4health has shown how existing components for secondary use of structured data can be complemented with text-mining in a privacy compliant manner. The cloud-computing paradigm allows a flexible and dynamically adaptable service provision that facilitates the adoption of services by data providers without own investments in respective hardware resources and software tools.

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Structured Reporting of Medical Findings: Evaluation of a System in Gastroenterology

1992 ◽  
Vol 31 (04) ◽  
pp. 268-274 ◽  
Author(s):  
W. Gaus ◽  
J. G. Wechsler ◽  
P. Janowitz ◽  
J. Tudyka ◽  
W. Kratzer ◽  
...  
Keyword(s):  
Structured Data ◽  
Structured Reporting ◽  
Free Text ◽  
Clinical Documentation ◽  
Abdominal Sonography ◽  
Technical Examination ◽  
Medical Reports ◽  
Time Required ◽  
Upper Abdominal ◽  
Structured Approach

Abstract:A system using structured reporting of findings was developed for the preparation of medical reports and for clinical documentation purposes in upper abdominal sonography, and evaluated in the course of routine use. The evaluation focussed on the following parameters: completeness and correctness of the entered data, the proportion of free text, the validity and objectivity of the documentation, user acceptance, and time required. The completeness in the case of two clinically relevant parameters could be compared with an already existing database containing freely dictated reports. The results confirmed the hypothesis that, for the description of results of a technical examination, structured data reporting is a viable alternative to free-text dictation. For the application evaluated, there is even evidence of the superiority of a structured approach. The system can be put to use in related areas of application.

Stalkers: Specialists Versus Generalists

2021 ◽  
pp. 088626052110235
Author(s):  
Lorraine Sheridan ◽  
Martyna Bendlin ◽  
Paul House
Keyword(s):  
Domestic Violence ◽  
Western Australia ◽  
Police Force ◽  
Restraining Orders ◽  
Police Data

Abstract It is known that many domestic violence (DV) offenders also commit violent and nonviolent offences that are not domestic in nature. Stalking frequently evolves from DV contexts. The present study used police data to explore (i) the extent to which stalking offenders in Western Australia specialize in stalking, (ii) the frequency of involvement in DV offending by stalking offenders, and (iii) the types of offences that co-occur with stalking offences. The dataset covered 404 individuals who were identified by the Western Australia Police Force as the offender for a stalking offence between January 1st, 2003 and July 30th, 2017. Only a minority of the offenders specialized in stalking, with the majority offending in other ways against the index victim and also offending against others via a broad range of offences. Although less than 10% were recorded as having carried out domestic assaults, more than half had broken restraining orders. Like DV offenders, the stalkers in this sample were largely generalist offenders. It was not clear, however, what proportion of offences against the same index victim were directly related to stalking. Stalking is a course of conduct that often involves individual acts that may be offences in themselves. What is clearer is the finding that for many stalkers, stalking forms part of a wider pattern of antisocial activity. Those stalkers who do not specialize in stalking may be less likely to benefit from intervention efforts that are focused solely on stalking.

Complaint management model of manufacturing products using text mining and potential failure identification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ririn Diar Astanti ◽  
Ivana Carissa Sutanto ◽  
The Jin Ai
Keyword(s):  
Quality Management ◽  
Text Mining ◽  
Failure Mode ◽  
Management System ◽  
Main Part ◽  
Mining Method ◽  
Content Type ◽  
Complaint Management ◽  
Potential Failure ◽  
Failure Identification

PurposeThis paper aims to propose a framework on complaint management system for quality management by applying the text mining method and potential failure identification that can support organization learning (OL). Customer complaints in the form of email text is the input of the framework, while the most frequent complaints are visualized using a Pareto diagram. The company can learn from this Pareto diagram and take action to improve their process.Design/methodology/approachThe first main part of the framework is creating a defect database from potential failure identification, which is the initial part of the failure mode and effect analysis technique. The second main part is the text mining of customer email complaints. The last part of the framework is matching the result of text mining with the defect database and presenting in the form of a Pareto diagram. After the framework is proposed, a case study is conducted to illustrate the applicability of the proposed method.FindingsBy using the defect database, the framework can interpret the customer email complaints into the list of most defect complained by customer using a Pareto diagram. The results of the Pareto diagram, based on the results of text mining of consumer complaints via email, can be used by a company to learn from complaint and to analyze the potential failure mode. This analysis helps company to take anticipatory action for avoiding potential failure mode happening in the future.Originality/valueThe framework on complaint management system for quality management by applying the text mining method and potential failure identification is proposed for the first time in this paper.

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