Automatic Mining of Symptom Severity from Psychiatric Evaluation Notes

Objectives: As electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free-text narrative aiming to support epidemiological research and clinical decision-making. In this paper we explore extraction of explicit mentions of symptom severity from initial psychiatric evaluation records. We use the data provided by the 2016 CGES N-GRID NLP Shared Task Track 2, which contains 541 records manually annotated for symptom severity according to the Research Domain Criteria (RDoC). Methods: We designed and implemented three automatic methods: a knowledge-driven approach relying on local lexicalized rules based on common syntactic patterns in text suggesting positive valence symptoms; a
machine learning method using a neural network; and a hybrid approach combining the first two methods with a neural network. Results: The results on an unseen evaluation set of 216 psychiatric evaluation records showed a performance of 80.1% for the rule-based method, 73.3% for the machine-learning approach and 72.0% for the hybrid one. Conclusions: While more work is needed to improve the accuracy, the results are encouraging and indicate that automated text mining methods can be used to classify mental health symptom severity from free text psychiatric notes to support epidemiological and clinical research.