Detection of High-Risk Depression Groups Based on Eye-Tracking Data

Predicting Suicide Risk Using Facial Action Units : A Machine Learning Approach to Objective and Large-Scale Screening

“…In clinical practice, the assessment of suicide risk is in uenced by the experience and cultural background of healthcare professionals, leading to some degree of subjective bias; it is also challenging to conduct large-scale screenings in various populations due to limitations pertaining to time and location. In this context, scholars have shown interest in identifying easily accessible objective indicators such as voice metrics, head movements, facial features, gait, serum markers, and salivary hormones, [7][8][9][10][11] and signi cant progress has been made in this area. Braithwaite 12 validated suicide models based on Twitter data and found that machine learning algorithms could effectively identify individuals at risk of suicide.…”