Making sense of violence risk predictions using clinical notes
This work addresses the trustworthiness and generalizability of violence risk classifiers for psychiatric practitioners, but it is incremental as it builds on existing methods without introducing major innovations.
The paper tackled the problem of improving violence risk assessment in psychiatric institutions by analyzing clinical notes from electronic health records, focusing on understanding classifier quality through topic models and evaluation metrics to enhance model generalizability.
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 to work on 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.