Team 9: A Comparison of Simple vs. Complex Models for Suicide Risk Assessment

Morales, Michelle; Dey, Prajjalita; Kohli, Kriti

doi:10.18653/v1/2021.clpsych-1.11

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…One of the main reasons for the continuous use of traditional machine learning methods could be the dataset size (Morales et al, 2021 ), where training a deep neural network with limited data could make the model overfit and not generalize well on the unseen data. The need for an interpretable outcome can also be highlighted as a valid reason for the continuous use of traditional machine learning systems in the mental illness and suicide ideation detection domain.…”

Section: Related Workmentioning

confidence: 99%

Multi-task learning to detect suicide ideation and mental disorders among social media users

Buddhitha

Inkpen

2023

Front. Res. Metr. Anal.

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Mental disorders and suicide are considered global health problems faced by many countries worldwide. Even though advancements have been made to improve mental wellbeing through research, there is room for improvement. Using Artificial Intelligence to early detect individuals susceptible to mental illness and suicide ideation based on their social media postings is one way to start. This research investigates the effectiveness of using a shared representation to automatically extract features between the two different yet related tasks of mental illness and suicide ideation detection using data in parallel from social media platforms with different distributions. In addition to discovering the shared features between users with suicidal thoughts and users who self-declared a single mental disorder, we further investigate the impact of comorbidity on suicide ideation and use two datasets during inference to test the generalizability of the trained models and provide satisfactory evidence to validate the increased predictive accurateness of suicide risk when using data from users diagnosed with multiple mental disorders compared to a single mental disorder for the mental illness detection task. Our results also demonstrate different mental disorders' impact on suicidal risk and discover a noticeable impact when using data from users diagnosed with Post-Traumatic Stress Disorder. We use multi-task learning (MTL) with soft and hard parameter sharing to produce state-of-the-art results for detecting users with suicide ideation who require urgent attention. We further improve the predictability of the proposed model by demonstrating the effectiveness of cross-platform knowledge sharing and predefined auxiliary inputs.

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Section: Related Workmentioning

confidence: 99%

Multi-task learning to detect suicide ideation and mental disorders among social media users

Buddhitha

Inkpen

2023

Front. Res. Metr. Anal.

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Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task

MacAvaney¹,

Mittu²,

Coppersmith³

et al. 2021

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

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Progress on NLP for mental health -indeed, for healthcare in general -is hampered by obstacles to shared, community-level access to relevant data. We report on what is, to our knowledge, the first attempt to address this problem in mental health by conducting a shared task using sensitive data in a secure data enclave. Participating teams received access to Twitter posts donated for research, including data from users with and without suicide attempts, and did all work with the dataset entirely within a secure computational environment. We discuss the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data.

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Team 9: A Comparison of Simple vs. Complex Models for Suicide Risk Assessment

Cited by 2 publications

References 8 publications

Multi-task learning to detect suicide ideation and mental disorders among social media users

Multi-task learning to detect suicide ideation and mental disorders among social media users

Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task

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