How can machine learning identify suicidal ideation from user's texts? Towards the explanation of the Boamente system

Oliveira, Adonias Caetano de; Diniz, Evandro J. S.; Teixeira, Silmar; Teles, Ariel Soares

doi:10.1016/j.procs.2022.09.093

Cited by 3 publications

(2 citation statements)

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“…Through LIWc, it is possible to identify distinctive features of suicidal thoughts ( Stirman and Pennebaker, 2001 ; Fernández-Cabana et al, 2013 ). Plus, machine learning and artificial intelligence had already been applied in trying to assess suicidal risk from social network ( Castillo-Sánchez et al, 2020 ; de Oliveira et al, 2022 ). Psycholinguistic analysis of posts and tweets had also already been applied to assess emotion expression and somatosensory processes in the context of traumatic experiences ( Monzani et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Suicide is one of the leading causes of death amongst adolescents and young adults (i.e., 15-29 years old), accounting for 8.5% of all deaths worldwide (World Health Organization, 2014). The developmental shift from childhood to adolescence marks a sharp increase in emotional and behavioural problems that, in combination with individual and environmental risk factors (e.g., mood disorders, preoccupation with death, family history of psychopathology or suicidal behavior (SB), poverty, family discord, and exposure to abuse or neglect) might contribute to causing suicidal thoughts and behaviours (Pfeffer, 1997;Hawton et al, 2003;de Leo and Heller, 2004;Tishler et al, 2007). Noteworthy, during the last two decades, there has been an increase in youth suicide, and adolescents appear to be particularly exposed to the onset of suicidal thoughts and behaviors (Nugent et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Issues and opportunities of digital phenotyping: ecological momentary assessment and behavioral sensing in protecting the young from suicide

et al. 2023

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Digital phenotyping refers to the collection of real-time biometric and personal data on digital tools, mainly smartphones, and wearables, to measure behaviors and variables that can be used as a proxy for complex psychophysiological conditions. Digital phenotyping might be used for diagnosis, clinical assessment, predicting changes and trajectories in psychological clinical conditions, and delivering tailored interventions according to individual real-time data. Recent works pointed out the possibility of using such an approach in the field of suicide risk in high-suicide-risk patients. Among the possible targets of such interventions, adolescence might be a population of interest, since they display higher odds of committing suicide and impulsive behaviors. The present work systematizes the available evidence of the data that might be used for digital phenotyping in the field of adolescent suicide and provides insight into possible personalized approaches for monitoring and treating suicidal risk or predicting risk trajectories. Specifically, the authors first define the field of digital phenotyping and its features, secondly, they organize the available literature to gather all the digital indexes (active and passive data) that can provide reliable information on the increase in the suicidal odds, lastly, they discuss the challenges and future directions of such an approach, together with its ethical implications.

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