Bibliographic Details
| Title: |
Deep learning models can predict violence and threats against healthcare providers using clinical notes |
| Authors: |
Nicholas J. Dobbins, Jacqueline Chipkin, Tim Byrne, Omar Ghabra, Julia Siar, Mitchell Sauder, R. Michael Huijon, Taylor M. Black |
| Source: |
npj Mental Health Research, Vol 3, Iss 1, Pp 1-8 (2024) |
| Publisher Information: |
Nature Portfolio, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Therapeutics. Psychotherapy |
| Subject Terms: |
Therapeutics. Psychotherapy, RC475-489 |
| More Details: |
Abstract Violence, verbal abuse, threats, and sexual harassment of healthcare providers by patients is a major challenge for healthcare organizations around the world, contributing to staff turnover, distress, absenteeism, reduced job satisfaction, and worsening mental and physical health. To enable interventions prior to possible violent episodes, we trained two deep learning models to predict violence against healthcare workers 3 days prior to violent events for case and control patients. The first model is a document classification model using clinical notes, and the second is a baseline regression model using largely structured data. Our document classification model achieved an F1 score of 0.75 while our model using structured data achieved an F1 of 0.72, both exceeding the predictive performance of a psychiatry team who reviewed the same documents (0.5 F1). To aid in the explainability and understanding of risk factors for violent events, we additionally trained a named entity recognition classifier on annotations of the same corpus, which achieved an overall F1 of 0.7. This study demonstrates the first deep learning model capable of predicting violent events within healthcare settings using clinical notes, surpassing the first published baseline of human experts. We anticipate our methods can be generalized and extended to enable intervention at other hospital systems. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2731-4251 |
| Relation: |
https://doaj.org/toc/2731-4251 |
| DOI: |
10.1038/s44184-024-00105-7 |
| Access URL: |
https://doaj.org/article/f4f839b28d394e4b9bbd7e3473d55876 |
| Accession Number: |
edsdoj.f4f839b28d394e4b9bbd7e3473d55876 |
| Database: |
Directory of Open Access Journals |
