Jyoti Sanyal
Biographic Data
| ID | 7685165 |
|---|---|
| NAME | Jyoti Sanyal |
| GIVEN NAMES | Jyoti |
| FAMILY NAME | Sanyal |
| SIGNATURE | SANYAL J |
| AFFILIATIONS | NIHR Maudsley Dementia Biomedical Research Unit |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis
We 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 ex…
An observational study of system-level changes to improve the recording of very brief advice for smoking cessation in an inpatient mental health setting
Financial incentives and the recording of incentivised outcomes, the comprehensive smoke-free policy, and the electronic referral system, were associated with increases in recording individual VBA elements, but other changes to the EHCR were not. System-level changes may facilitate staff recording of VBA provision in mental health settings
No prominent works on this page.
An observational study of system-level changes to improve the recording of very brief advice for smoking cessation in an inpatient mental health setting
Financial incentives and the recording of incentivised outcomes, the comprehensive smoke-free policy, and the electronic referral system, were associated with increases in recording individual VBA elements, but other changes to the EHCR were not. System-level changes may facilitate staff recording of VBA provision in mental health settings
Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis
We 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 ex…
Medicine (2 works) · Mental health (2 works) · Psychiatry (2 works) · Algorithm (1 work) · Artificial Intelligence (1 work) · Artificial neural network (1 work) · Ask price (1 work) · Computer Science (1 work) · Crime Patterns and Interventions (1 work) · Environmental health (1 work)