attitude toward suicide. This study aimed to extract factors from the Attitude Toward Suicide Scale (ATTS) and investigate the relationship between attitudes toward suicide and suicidal behavior (i.e., suicidal idea, plan, and attempt) by using a representative sample of Korean adults.Methods Three thousand Koreans aged 19 to 75 years were surveyed cross-sectionally in 2013 and 2018. The data collected were subjected to exploratory factor analysis. Extracted attitude factors were compared using a suicidal behavior continuum. Univariate and multivariate logistic models were constructed to compare the association between attitude factors and suicidal behaviors.Results Among the participants, 477 (15.9%) experienced suicidal idea only, 85 (2.8%) had a suicidal plan without attempt, and 58 (1.9%) attempted suicide. Four meaningful factors were extracted from the factor analysis: “permissiveness,” “unjustified behavior,” “preventability/ readiness to help,” and “loneliness.” “Permissiveness,” “unjustified behavior,” and “loneliness” factors showed significant trends across the suicidal behavior continuum. Permissive attitude toward suicide increased the odds of suicidal idea, suicidal plan, and suicide attempt (adjusted odds ratio [aOR]=1.49, 95% confidence interval [CI]=1.25–1.79; aOR=2.79, 95% CI=1.84–4.25; aOR=2.67, 95% CI=1.65–4.33), while attitude toward suicide as unjustified behavior decreased the odds of suicidal ideation and attempt (aOR=0.79, 95% CI=0.67–0.94; aOR=0.64, 95% CI=0.42–0.99).Conclusion A significant association was found between attitude toward suicide and suicidal behaviors. Attitude toward suicide is a modifiable factor that can be used to develop prevention policies.
Background Assessing a patient’s suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. Objective This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. Methods We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months. Results A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets. Conclusions Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.
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