Suicide is a leading cause of death globally. To improve prevention and treatment of suicidal ideation and suicidal behavior, the identification of reliable mechanisms and pychosocial and other risk factors is critical. To date, however, our ability to assess suicide risk has been limited.
MULTICAST, a Sinergia project funded by the SNSF, consists of a multidisciplinary research team who will tackle the task of precise prediction of and effective treatment strategies for suicidality. The project is co-hosted by the Psychiatric University Hospital (UZH), the Department of Psychology (UZH), the LiZZ and the German Department of the University of Zurich and the Department of Emergency Medicine (Columbia University).
Prediction of suicidal ideation will incorporate key linguistic, psychological, clinical and neurobiological features. The linguistic features will include a unique focus on syntactic variables, which have never been accounted for in previous research (as shown in a review paper by Homan et al., 2022). In order to examine and assess these linguistic features, LiZZ researchers will investigate the speech of a highly vulnerable cohort: psychiatric patients post-discharge from a suicide-related inpatient psychiatry stay. Besides linguistic analysis, psychological and digital behavior, as well as neurobiological indices will be incorporated for the task of prediction.
The findings and the assessment of the data will be incorporated in the development of machine learning algorithms that will predict suicidal ideation and suicidal behavior.
Our results will refine psychological and linguistic theory and help mental health practitioners to identify and reach out to vulnerable individuals at risk of suicidal ideation and suicidal behavior in a timely manner. The MULTICAST project may thus save lives.