Citation

George A, Johnson D, Carenini G, Eslami A, Ng R, Portales-Casamar E. AMIA Annu. Symp. Proc. 2021; 2021: 229-237.

Copyright

(Copyright © 2021, American Medical Informatics Association)

DOI

unavailable

PMID

34457137

Abstract

Understanding and identifying the risk factors associated with suicide in youth experiencing mental health concerns is paramount to early intervention. 45% of patients are admitted annually for suicidality at BC Children's Hospital. Natural Language Processing (NLP) approaches have been applied with moderate success to psychiatric clinical notes to predict suicidality. Our objective was to explore whether machine-learning-based sentiment analysis could be informative in such a prediction task. We developed a psychiatry-relevant lexicon and identified specific categories of words, such as thought content and thought process that had significantly different polarity between suicidal and non-suicidal cases. In addition, we demonstrated that the individual words with their associated polarity can be used as features in classification models and carry informative content to differentiate between suicidal and non-suicidal cases. In conclusion, our study reveals that there is much value in applying NLP to psychiatric clinical notes and suicidal prediction.

Language: en