# Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes

arXiv: https://arxiv.org/abs/2204.13535 · HTML: https://mlsanity.com/paper/2204.13535/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [BERTje: A Dutch BERT Model](https://mlsanity.com/paper/1912.09582/) (2019)
- [Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching](https://mlsanity.com/paper/2004.12297/) (2020)
- [The merits of Universal Language Model Fine-tuning for Small Datasets -- a case with Dutch book reviews](https://mlsanity.com/paper/1910.00896/) (2019)
- [Grid Search Hyperparameter Benchmarking of BERT, ALBERT, and LongFormer on DuoRC](https://mlsanity.com/paper/2101.06326/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting](https://mlsanity.com/paper/2205.10234/) (2022)

## Abstract (arXiv)

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.