Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis.

Introduction: Experiences of violence are important risk factors for worse outcome in people with mental health conditions; however, they are not routinely collected be mental health services, so their ascertainment depends on extraction from text fields with natural language processing (NLP) algorithms.

Methods: Applying previously developed neural network algorithms to routine mental healthcare records, we sought to describe the distribution of recorded violence victimisation by demographic and diagnostic characteristics. We ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019.

Investigating the relationship between thought interference, somatic passivity and outcomes in patients with psychosis: a natural language processing approach using a clinical records search platform in south London.

Objectives: We aimed to apply natural language processing algorithms in routine healthcare records to identify reported somatic passivity (external control of sensations, actions and impulses) and thought interference symptoms (thought broadcasting, insertion, withdrawal), first-rank symptoms traditionally central to diagnosing schizophrenia, and determine associations with prognosis by analysing routine outcomes.

Design: Four algorithms were developed on deidentified mental healthcare data and applied to ascertain recorded symptoms over the 3 months following first presentation to a mental healthcare provider in a cohort of patients with a primary schizophreniform disorder (ICD-10 F20-F29) diagnosis.

Setting And Participants: From the electronic health records of a large secondary mental healthcare provider in south London, 9323 patients were ascertained from 2007 to the data extraction date (25 February 2020).

Association between depressive symptoms and cognitive-behavioural therapy receipt within a psychosis sample: a cross-sectional study.

Objectives: To examine whether depressive symptoms predict receipt of cognitive-behavioural therapy for psychosis (CBTp) in individuals with psychosis.

Design: Retrospective cross-sectional analysis of electronic health records (EHRs) of a clinical cohort.

Setting: A secondary National Health Service mental healthcare service serving four boroughs of south London, UK.

Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK.

Objectives: We set out to develop, evaluate and implement a novel application using natural language processing to text mine occupations from the free-text of psychiatric clinical notes.

Design: Development and validation of a natural language processing application using General Architecture for Text Engineering software to extract occupations from de-identified clinical records.

Setting And Participants: Electronic health records from a large secondary mental healthcare provider in south London, accessed through the Clinical Record Interactive Search platform.

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

Background: How neighbourhood characteristics affect the physical safety of people with mental illness is unclear.

Aims: To examine neighbourhood effects on physical victimisation towards people using mental health services.

Method: We developed and evaluated a machine-learning-derived free-text-based natural language processing (NLP) algorithm to ascertain clinical text referring to physical victimisation.

An observational study of system-level changes to improve the recording of very brief advice for smoking cessation in an inpatient mental health setting.

Background: Smoking prevalence among people with psychosis remains high. Providing Very Brief Advice (VBA) comprising: i) ASK, identifying a patient's smoking status ii) ADVISE, advising on the best way to stop and iii) ACT (OFFER), offering a referral to specialist smoking cessation support, increases quit attempts in the general population. We assessed whether system-level changes in a UK mental health organisation improved the recording of the provision of ASK, ADVISE, ACT (OFFER) and consent to referral to specialist smoking cessation support (ACT (CONSENT)).

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder.

Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP) applied to Electronic Health Records (EHRs) presents an opportunity to create large datasets to facilitate research in this area. This is a challenging endeavour however, because of the wide range of ways in which these symptoms are recorded, and the overlap of terms used to describe OCS with those used to describe other conditions.

Recorded poor insight as a predictor of service use outcomes: cohort study of patients with first-episode psychosis in a large mental healthcare database.

Objectives: To investigate recorded poor insight in relation to mental health and service use outcomes in a cohort with first-episode psychosis.

Design: We developed a natural language processing algorithm to ascertain statements of poor or diminished insight and tested this in a cohort of patients with first-episode psychosis.

Setting: The clinical record text at the South London and Maudsley National Health Service Trust in the UK was used.