Citation

Bantilan N, Malgaroli M, Ray B, Hull TD. Psychother. Res. 2020; ePub(ePub): 1-11.

Affiliation

Columbia University, New York, USA.

Copyright

(Copyright © 2020, Society for Psychotherapy Research, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/10503307.2020.1781952

PMID

32558625

Abstract

Objective: To design a Natural Language Processing (NLP) algorithm capable of detecting suicide content from patients' written communication to their therapists, to support rapid response and clinical decision making in telehealth settings. Method: A training dataset of therapy transcripts for 1,864 patients was established by detecting patient content endorsing suicidality using a proxy-model anchored on therapists' suicide prevention interventions; human expert raters then assessed the level of suicide risk endorsed by patients identified by the proxy-model (i.e., no risk, risk factors, ideation, method, or plan). A bag-of-words classification model was then iteratively built using the annotations from the expert raters to detect suicide risk level in 85,216 labeled patients' sentences from the training dataset. Results: The final NLP model identified risk-related content from non-risk content with good accuracy (AUC = 82.78). Conclusions: Risk for suicide could be reliably identified by the NLP algorithm. The risk detection model could assist telehealth clinicians in providing crisis resources in a timely manner. This modeling approach could also be applied to other psychotherapy research tasks to assist in the understanding of how the psychotherapy process unfolds for each patient and therapist.

Language: en

Keywords

digital health; machine learning; natural language processing; suicide; telemedicine