Making sense of violence risk predictions using clinical notes

Pablo Mosteiro, Emil Rijcken, Kalliopi Zervanou, Uzay Kaymak, Floortje Scheepers, Marco Spruit

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
5 Downloads (Pure)


Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records (EHR) are valuable resources that are seldom used to their full potential. Previous studies have attempted to assess violence risk in psychiatric patients using such notes, with acceptable performance. However, they do not explain why classification works and how it can be improved. We explore two methods to better understand the quality of a classifier in the context of clinical note analysis: random forests using topic models, and choice of evaluation metric. These methods allow us to understand both our data and our methodology more profoundly, setting up the groundwork for improved models that build upon this understanding. This is particularly important when it comes to the generalizability of evaluated classifiers to new data, a trustworthiness problem that is of great interest due to the increased availability of new data in electronic format.
Original languageEnglish
Title of host publicationHealth Information Science
Subtitle of host publicationProceedings of the 9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20–23, 2020
EditorsZhisheng Huang, Siuly Siuly, Hua Wang, Yanchun Zhang, Rui Zhou
Place of PublicationCham
Number of pages12
ISBN (Electronic)978-3-030-61951-0
ISBN (Print)978-3-030-61950-3
Publication statusPublished - 2020
Event9th International Conference on Health Information Science (HIS 2020) - Amsterdam and Leiden, Netherlands
Duration: 20 Oct 202023 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12435 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Health Information Science (HIS 2020)
CityAmsterdam and Leiden


  • Document classification
  • Electronic Health Records
  • Interpretability
  • LDA
  • Natural Language Processing
  • Random forests
  • Topic modeling


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