Natural Language Processing: Interrogating free text in mental healthcare records to capture experiences of violence
Violence can be categorised in a variety of ways for example physical, sexual, emotional, and domestic but all cause significant physical and mental morbidity within general populations. Individuals with a severe mental illness have been found to be significantly more likely to experience domestic, physical, and sexual violence compared to the general population. For these individuals, experiences of violence are important risk factors however, this is not routinely collected by mental health services.
In general data on all forms of violence has been inadequately available from healthcare records. This is partly due to the lack of routine enquiry by professionals at points of clinical contact, and partly because instances of violence are difficult to identify in healthcare data in the absence of specific coding systems.
A general challenge for using health records data for research is that the most valuable and granular information is frequently contained in text fields (e.g., routine case notes, clinical correspondence) rather than in pre-structured fields; this includes mentions of violence whether experienced as a victim or perpetrated. Capturing violence experiences across mental healthcare settings can be challenging because most instances are likely to be recorded as unstructured text data. Therefore, natural language processing (NLP), is increasingly in use to extract information automatically from unstructured text in electronic health records, particularly in mental healthcare, on clinical entities.
Dr Ava Mason from Kings College London and VISION researchers Professor Robert Stewart, Dr Angus Roberts, Dr Lifang Li, and Dr Vishal Bhavsar worked with colleagues to apply NLP across different clinical samples to investigate mentions of violence. They ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019. Descriptive and regression analyses were conducted to investigate variation by age, sex, ethnic group, and diagnostic category.
Results showed that patients with a mood disorder, personality disorder, schizophrenia spectrum disorder or PTSD had a significantly increased likelihood of victimisation compared to those with other mental health diagnoses. Additionally, patients from minority ethnic groups for Black and Asian had significantly higher likelihood of recorded violence victimisation compared to White groups. Males were significantly less likely to have reported recorded violence victimisation than females.
The researchers demonstrated the successful deployment of machine learning based NLP algorithms to ascertain important entities for outcome prediction in mental healthcare. The observed distributions highlight which sex, ethnicity and diagnostic groups had more records of violence victimisation. Further development of these algorithms could usefully capture broader experiences, such as differentiating more efficiently between witnessed, perpetrated and experienced violence and broader violence experiences like emotional abuse.
To download the paper: Frontiers | Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis
To cite: Mason AJC, Bhavsar V, Botelle R, Chandran D, Li L, Mascio A, Sanyal J, Kadra-Scalzo G, Roberts A, Williams M, Stewart R. Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis. Frontiers in Psychiatry 2024 Sep 10. doi:103389/fpsyt.2024.1181739
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