Text Classification to Inform Suicide Risk Assessment in Electronic Health Records

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

10 Citations (Scopus)
154 Downloads (Pure)


Assessing a patient's risk of an impending suicide attempt has been hampered by limited information about dynamic factors that change rapidly in the days leading up to an attempt. The storage of patient data in electronic health records (EHRs) has facilitated population-level risk assessment studies using machine learning techniques. Until recently, most such work has used only structured EHR data, to the exclusion of the unstructured text of clinical notes. In this article, we describe our experiments on suicide risk assessment, modelling the problem as a classification task. Given the wealth of text data in mental health EHRs, we aimed to assess the impact of using this data in distinguishing periods prior to a suicide attempt from those not preceding such an attempt. We compare three different feature sets, one structured and two text-based, and show that inclusion of text features significantly improves classification accuracy in suicide risk assessment.
Original languageEnglish
Title of host publicationStudies in Health Technology and Informatics
Number of pages5
Publication statusPublished - 21 Aug 2019


Dive into the research topics of 'Text Classification to Inform Suicide Risk Assessment in Electronic Health Records'. Together they form a unique fingerprint.

Cite this