Abstract
Inpatient violence is a common and severe problem within psychiatry. Knowing who might become violent can influence staffing levels and mitigate severity. Predictive machine learning models can assess each patient’s likelihood of becoming violent based on clinical notes. Yet, while machine learning models benefit from having more data, data availability is limited as hospitals typically do not share their data for privacy preservation. Federated Learning (FL) can overcome the problem of data limitation by training models in a decentralised manner, without disclosing data between collaborators. However, although several FL approaches exist, none of these train Natural Language Processing models on clinical notes. In this work, we investigate the application of Federated Learning to clinical Natural Language Processing, applied to the task of Violence Risk Assessment by simulating a cross-institutional psychiatric setting. We train and compare four models: two local models, a federated model and a data-centralised model. Our results indicate that the federated model outperforms the local models and has similar performance as the data-centralised model. These findings suggest that Federated Learning can be used successfully in a cross-institutional setting and is a step towards new applications of Federated Learning based on clinical notes.
Original language | English |
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Article number | 116720 |
Pages (from-to) | 1-9 |
Number of pages | 9 |
Journal | Expert Systems with Applications |
Volume | 199 |
DOIs | |
Publication status | Published - 1 Aug 2022 |
Bibliographical note
Funding Information:This work was supported by Utrecht University, The Netherlands , the PsyData team from UMC Utrecht, The Netherlands , and KPMG, The Netherlands n.v. Useful discussions were held with Sebastian Mildiner and Laurens Breij.
Publisher Copyright:
© 2022 The Authors
Keywords
- Clinical notes
- Federated learning
- Neural networks
- Psychiatry
- Violence prediction