Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes

Authors

Pablo Mosteiro^{1, *,} , Emil Rijcken^{2, 1}, Kalliopi Zervanou^2, , Uzay Kaymak^2, , Floortje Scheepers³, Marco Spruit^{1, 4, 5,}

¹Utrecht University, Utrecht, The Netherlands

²Eindhoven University of Technology, Eindhoven, The Netherlands

³University Medical Center Utrecht, Utrecht, The Netherlands

⁴Leiden University Medical Center, Leiden, The Netherlands

⁵Leiden Institute of Advanced Computer Science, Leiden, The Netherlands

^*Corresponding author. Email: p.mosteiro@uu.nl

DOI

10.2991/jaims.d.210225.001How to use a DOI?

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.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Pablo Mosteiro
AU  - Emil Rijcken
AU  - Kalliopi Zervanou
AU  - Uzay Kaymak
AU  - Floortje Scheepers
AU  - Marco Spruit
PY  - 2021
DA  - 2021/03/04
TI  - Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes
JO  - Journal of Artificial Intelligence for Medical Sciences
SP  - 44
EP  - 54
VL  - 2
IS  - 1-2
SN  - 2666-1470
UR  - https://doi.org/10.2991/jaims.d.210225.001
DO  - 10.2991/jaims.d.210225.001
ID  - Mosteiro2021
ER  -