Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis
IntroductionExperiences 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) algorith...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-09-01
|
| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1181739/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849730275059171328 |
|---|---|
| author | Ava J. C. Mason Vishal Bhavsar Vishal Bhavsar Riley Botelle David Chandran Lifang Li Aurelie Mascio Jyoti Sanyal Giouliana Kadra-Scalzo Angus Roberts Marcus Williams Marcus Williams Robert Stewart Robert Stewart |
| author_facet | Ava J. C. Mason Vishal Bhavsar Vishal Bhavsar Riley Botelle David Chandran Lifang Li Aurelie Mascio Jyoti Sanyal Giouliana Kadra-Scalzo Angus Roberts Marcus Williams Marcus Williams Robert Stewart Robert Stewart |
| author_sort | Ava J. C. Mason |
| collection | DOAJ |
| description | IntroductionExperiences 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.MethodsApplying 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. Descriptive and regression analyses were conducted to investigate variation by age, sex, ethnic group, and diagnostic category (ICD-10 F chapter sub-headings plus post-traumatic stress disorder (PTSD) as a specific condition).ResultsPatients with a mood disorder (adjusted odds ratio 1.63, 1.55-1.72), personality disorder (4.03, 3.65-4.45), schizophrenia spectrum disorder (1.84, 1.74-1.95) or PTSD (2.36, 2.08-2.69) had a significantly increased likelihood of victimisation compared to those with other mental health diagnoses. Additionally, patients from minority ethnic groups (1.10 (1.02-1.20) for Black, 1.40 (1.31-1.49) for Asian compared to White groups) had significantly higher likelihood of recorded violence victimisation. Males were significantly less likely to have reported recorded violence victimisation (0.44, 0.42-0.45) than females.DiscussionWe thus demonstrate 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. |
| format | Article |
| id | doaj-art-9f9c3b2bdef9483c95da15f86765d52f |
| institution | DOAJ |
| issn | 1664-0640 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-9f9c3b2bdef9483c95da15f86765d52f2025-08-20T03:08:56ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402024-09-011510.3389/fpsyt.2024.11817391181739Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosisAva J. C. Mason0Vishal Bhavsar1Vishal Bhavsar2Riley Botelle3David Chandran4Lifang Li5Aurelie Mascio6Jyoti Sanyal7Giouliana Kadra-Scalzo8Angus Roberts9Marcus Williams10Marcus Williams11Robert Stewart12Robert Stewart13King’s College London Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, United KingdomKing’s College London Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, United KingdomBiomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United KingdomKing’s College London Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, United KingdomKing’s College London Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, United KingdomKing’s College London Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, United KingdomKing’s College London Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, United KingdomBiomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United KingdomKing’s College London Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, United KingdomKing’s College London Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, United KingdomBiomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United KingdomSandwell and West Birmingham Hospitals National Health Service (NHS) Trust, West Bromwich, United KingdomKing’s College London Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, United KingdomBiomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United KingdomIntroductionExperiences 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.MethodsApplying 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. Descriptive and regression analyses were conducted to investigate variation by age, sex, ethnic group, and diagnostic category (ICD-10 F chapter sub-headings plus post-traumatic stress disorder (PTSD) as a specific condition).ResultsPatients with a mood disorder (adjusted odds ratio 1.63, 1.55-1.72), personality disorder (4.03, 3.65-4.45), schizophrenia spectrum disorder (1.84, 1.74-1.95) or PTSD (2.36, 2.08-2.69) had a significantly increased likelihood of victimisation compared to those with other mental health diagnoses. Additionally, patients from minority ethnic groups (1.10 (1.02-1.20) for Black, 1.40 (1.31-1.49) for Asian compared to White groups) had significantly higher likelihood of recorded violence victimisation. Males were significantly less likely to have reported recorded violence victimisation (0.44, 0.42-0.45) than females.DiscussionWe thus demonstrate 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1181739/fullnatural language processingvictimisationmental health recordsCRISviolence |
| spellingShingle | Ava J. C. Mason Vishal Bhavsar Vishal Bhavsar Riley Botelle David Chandran Lifang Li Aurelie Mascio Jyoti Sanyal Giouliana Kadra-Scalzo Angus Roberts Marcus Williams Marcus Williams Robert Stewart Robert Stewart Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis Frontiers in Psychiatry natural language processing victimisation mental health records CRIS violence |
| title | Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis |
| title_full | Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis |
| title_fullStr | Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis |
| title_full_unstemmed | Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis |
| title_short | Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis |
| title_sort | applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis |
| topic | natural language processing victimisation mental health records CRIS violence |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1181739/full |
| work_keys_str_mv | AT avajcmason applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT vishalbhavsar applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT vishalbhavsar applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT rileybotelle applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT davidchandran applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT lifangli applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT aureliemascio applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT jyotisanyal applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT gioulianakadrascalzo applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT angusroberts applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT marcuswilliams applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT marcuswilliams applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT robertstewart applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis AT robertstewart applyingneuralnetworkalgorithmstoascertainreportedexperiencesofviolenceinroutinementalhealthcarerecordsanddistributionsofreportsbydiagnosis |