Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records are valuable resources capturing unique information, but are seldom used to their full potential. We explore conventional and deep machine learning methods to assess violence risk in psychiatric patients using practitioner notes. The performance of our best models is comparable to the currently used questionnaire-based method, with an area under the Receiver Operating Characteristic curve of approximately 0.8. We find that the deep-learning model BERTje performs worse than conventional machine learning methods. We also evaluate our data and our classifiers to understand the performance of our models better. This is particularly important for the applicability of evaluated classifiers to new data, and is also of great interest to practitioners, due to the increased availability of new data in electronic format.

利用临床记录进行暴力风险评估是有价值的，本研究探索传统和深度机器学习方法，使用临床记录评估精神病患的暴力风险，发现其表现与目前使用的基于问卷的方法相当，其中基于BERTje的深度学习模型的表现较差。

利用荷兰临床笔记进行暴力风险评估的机器学习