JMIR Publications

ABSTRACT

Background:

Assessing patients’ suicide risk is challenging, especially among those who deny suicidal ideation. Primary care providers have a poor agreement in screening suicide risk. Patients’ speech can provide more objective language-based clues about their underlying suicidal ideation. Text analysis to detect suicide risk from depression is lacking in the literature.

Objective:

To determine whether suicidal ideation can be detected via language features in clinical interviews for depression using natural language processing (NLP) and machine learning (ML).

Methods:

This cross-sectional study consisted of 305 participants (mean age=53.0±11.77 years; 57% female), of which 197 had lifetime depression and 108 were healthy subjects, collected between October 2020 and May 2022. This study was part of an ongoing research in characterizing depression with a case-control design. In this study, 236 participants were non-suicidal, while 56 and 13 had low and high suicide risks, respectively. The structured interview guide for the Hamilton Depression Rating Scale (HAMD) was adopted to assess suicide risk and depression severity. Suicide risk was clinician-rated based on a suicide-related question (H11). The interviews were transcribed and the words in participants’ verbal responses were translated into psychologically meaningful categories using Linguistic Inquiry and Word Count (LIWC).

Results:

Ordinal logistic regression reveals significant suicide-related language features in participants’ responses to the HAMD questions. Increased use of anger words when talking about work and activities posed the highest suicide risk (OR=2.91, 95% CI=1.22-8.55, P=.02). Random Forest models demonstrate that text analysis of the direct responses to H11 is more effective in identifying individuals with high risk (AUC=0.76-0.89, P<.001) and detecting suicide risk in general (including both low and high suicide risks) (AUC=0.83-0.92, P<.001). More importantly, suicide risk can be detected with satisfactory performance even without patients’ disclosure of suicidal ideation. Based on the response to the question on hypochrondriasis, ML models were trained to identify individuals with high suicide risk (AUC=0.76, P<.001).

Conclusions:

The findings examined the perspective of using NLP and ML to analyze the texts from clinical interviews for suicidality detection, which has the potential to provide more accurate and specific markers for suicidal ideation detection. The findings may pave the way for developing high-performance assessment of suicide risk for automated detection including online chatbot-based interviews for universal screening.