Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes

Authors

DOI:

https://doi.org/10.2991/jaims.d.210225.001

Keywords:

Natural language processing, Topic modeling, Electronic health records, BERT, Evaluation metrics, Interpretability, Document classification, LDA, Random forests

Abstract

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.

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Published

2021-05-05

How to Cite

1.
Mosteiro P, Rijcken E, Zervanou K, Kaymak U, Scheepers F, Spruit M. Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes. JAIMS [Internet]. 2021 May 5 [cited 2023 Feb. 4];2(1-2):44-5. Available from: http://oapublishing-jaims.com/jaims/article/view/64