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SIDVis: Designing visual interactive system for analyzing suicide ideation detection

conference contribution
posted on 2024-05-29, 01:37 authored by MR Islam, MKH Sakib, Anwaar Ulhaq, S Akter, J Zhou, D Asirvathamt
Suicide is a critical global issue that demands a comprehensive examination of factors such as mental illness, substance abuse, financial stress, and trauma. Effectively identifying individuals at risk is vital for intervention and prevention efforts. However, distinguishing suicidal ideation (SID) from non-suicidal language poses challenges. Existing research has addressed this issue, but limited attention has been given to visually interpretable and interactive systems tailored for SID. This study contributes to responsible AI by leveraging deep learning and machine learning techniques to enhance SID detection, enabling proactive interventions and support. In this paper, we introduce SIDVis, an interactive visualization system that improves performance and interpretability at the same time. The rigorous evaluation demonstrates that SIDVis not only outperforms existing methods in terms of accuracy but also provides an explanation for the responsible use of the underlying AI approach, demonstrating its potential to improve SID detection and intervention strategies.

Funding

Category 2 - Other Public Sector Grants Category

History

Start Page

384

End Page

389

Number of Pages

6

Start Date

2023-07-25

Finish Date

2023-07-28

eISSN

2375-0138

ISSN

1550-6037

ISBN-13

9798350341614

Location

Tampere, Finland

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

  • Yes

Open Access

  • No

Era Eligible

  • Yes

Name of Conference

2023 27th International Conference Information Visualisation (IV 2023)

Parent Title

Proceedings of the International Conference on Information Visualisation

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