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

本文研究应用联邦学习和自然语言处理于跨机构精神科领域进行暴力危险评估，并比较四个模型的性能，结果显示联邦学习模型表现最佳，可能为基于临床笔记的联邦学习应用开启了新的可能性。

模拟跨机构精神科设置中的暴力事件预测的联邦学习

Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting

Recently natural language processing (NLP) tools have been developed to
identify and extract salient risk indicators in electronic health records
(EHRs). Sentiment analysis, although widely used in non-medical areas for
improving decision making, has been studied minimally in the clinical setting.
In this study, we undertook, to our knowledge, the first domain adaptation of
sentiment analysis to psychiatric EHRs by defining psychiatric clinical
sentiment, performing an annotation project, and evaluating multiple
sentence-level sentiment machine learning (ML) models. Results indicate that
off-the-shelf sentiment analysis tools fail in identifying clinically positive
or negative polarity, and that the definition of clinical sentiment that we
provide is learnable with relatively small amounts of training data. This
project is an initial step towards further refining sentiment analysis methods
for clinical use. Our long-term objective is to incorporate the results of this
project as part of a machine learning model that predicts inpatient readmission
risk. We hope that this work will initiate a discussion concerning domain
adaptation of sentiment analysis to the clinical setting.

本研究首次将情感分析引入精神科领域，通过定义心理临床情感、标注和评估多个机器学习模型，得出结论：现有的情感分析工具不能准确识别临床正面或负面极性，而我们提供的临床情感定义可以用相对较少的训练数据进行学习。该项目是进一步改进临床使用情感分析方法的初步步骤，最终目标是将结果纳入机器学习模型以预测住院患者的再次入院风险。我们希望这项工作会引发有关情感分析领域适应临床应用的讨论。