The association between neighbourhood characteristics and physical victimisation in men and women with mental disorders

Bhavsar, Vishal; Sanyal, Jyoti; Patel, Rashmi; Shetty, Hitesh; Velupillai, Sumithra; Stewart, Robert; Broadbent, Matthew; MacCabe, James H.; Das‐Munshi, Jayati; Howard, Louise M.

doi:10.1192/bjo.2020.52

Cited by 6 publications

(11 citation statements)

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“…There is limited previous research examining NLP to extract violence-related information in EHRs but includes using text rules to ascertain violent behaviour as antecedents to supervised confinement 16 and the employment of a bag of words machine learning approach to extract information on physical assault victimisation in CRIS data. 28 Our approach captures a much broader range of experiences. We successfully developed an annotated dataset of clinical text references to interpersonal violence, using a multidisciplinary clinical academic group.…”

Section: Discussionmentioning

confidence: 99%

“…To our knowledge, this is the first study that has used a NLP approach to code free-text data from a large and diverse source of electronic mental health records to ascertain violence according to presence, agent and type. There is limited previous research examining NLP to extract violence-related information in EHRs but includes using text rules to ascertain violent behaviour as antecedents to supervised confinement16 and the employment of a bag of words machine learning approach to extract information on physical assault victimisation in CRIS data 28. Our approach captures a much broader range of experiences.…”

Section: Discussionmentioning

confidence: 99%

“…SLaM provides specialist mental health services to around 1.3 million residents of four boroughs in South-East London (Lambeth, Southwark, Lewisham and Croydon). The CRIS database was developed in 2008 and allows researchers to access structured data (such as demographic data from forms) and unstructured data (such as free text entered by clinicians based on clinical encounters) for mental health research 28–31. Currently, there are over 500 000 patient records represented in CRIS, CRIS, spanning 2007–present.…”

Section: Methodsmentioning

confidence: 99%

“…The CRIS database was developed in 2008 and allows researchers to access structured data (such as Open access demographic data from forms) and unstructured data (such as free text entered by clinicians based on clinical encounters) for mental health research. [28][29][30][31] Currently, there are over 500 000 patient records represented in CRIS, CRIS, spanning 2007-present.…”

Section: Data Sourcementioning

confidence: 99%

“…Previous NLP approaches to capturing violence in mental health clinical text have had significant limitations such as focusing on single forms of violence, that is, physical assault victimisation, 28 rather than capturing a broad range of possibly co-existing violence characteristics (eg, perpetration, victimisation, witnessing), forms of harm (physical, sexual) and the nature of the relationship between victim and perpetrator (domestic, community) in the same NLP approach. Additionally, previous literature has used limited data, for example, only inpatient data.…”

Section: Introductionmentioning

confidence: 99%

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Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study

et al. 2022

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ObjectiveThis paper evaluates the application of a natural language processing (NLP) model for extracting clinical text referring to interpersonal violence using electronic health records (EHRs) from a large mental healthcare provider.DesignA multidisciplinary team iteratively developed guidelines for annotating clinical text referring to violence. Keywords were used to generate a dataset which was annotated (ie, classified as affirmed, negated or irrelevant) for: presence of violence, patient status (ie, as perpetrator, witness and/or victim of violence) and violence type (domestic, physical and/or sexual). An NLP approach using a pretrained transformer model, BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) was fine-tuned on the annotated dataset and evaluated using 10-fold cross-validation.SettingWe used the Clinical Records Interactive Search (CRIS) database, comprising over 500 000 de-identified EHRs of patients within the South London and Maudsley NHS Foundation Trust, a specialist mental healthcare provider serving an urban catchment area.ParticipantsSearches of CRIS were carried out based on 17 predefined keywords. Randomly selected text fragments were taken from the results for each keyword, amounting to 3771 text fragments from the records of 2832 patients.Outcome measuresWe estimated precision, recall and F1 score for each NLP model. We examined sociodemographic and clinical variables in patients giving rise to the text data, and frequencies for each annotated violence characteristic.ResultsBinary classification models were developed for six labels (violence presence, perpetrator, victim, domestic, physical and sexual). Among annotations affirmed for the presence of any violence, 78% (1724) referred to physical violence, 61% (1350) referred to patients as perpetrator and 33% (731) to domestic violence. NLP models’ precision ranged from 89% (perpetrator) to 98% (sexual); recall ranged from 89% (victim, perpetrator) to 97% (sexual).ConclusionsState of the art NLP models can extract and classify clinical text on violence from EHRs at acceptable levels of scale, efficiency and accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Sourcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study

et al. 2022

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Systematic review of approaches to use of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions

Wilkinson

Sheets

Fitch

et al. 2021

Journal of Biomedical Informatics

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A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Wang

et al. 2022

npj Digit. Med.

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Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union’s funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP’s great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

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The association between neighbourhood characteristics and physical victimisation in men and women with mental disorders

Cited by 6 publications

References 45 publications

Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study

Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study

Systematic review of approaches to use of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

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