Sahil Walia

Work place: Solutions Architect, Snowflake, Inc., Atlanta, Georgia, USA

E-mail: waliasahil11@gmail.com

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Biography

Sahil Walia is a Data Solutions Architect at Snowflake, Inc., located in Georgia, USA. He works within the Professional Services division, assisting Snowflake's customers in optimizing, accelerating, and achieving their business objectives. Sahil holds a Master's degree in Information Management from Syracuse University. With over a decade of industry experience, he has successfully led numerous data migration and modernization projects that have resulted in cost-efficiency, improved performance, and streamlined data pipelines. Sahil is actively involved in the research community and has been a dedicated member of the IEEE.

Author Articles
Enhancing Suicide Risk Prediction through BERT: Leveraging Textual Biomarkers for Early Detection

By Karan Bajaj Mukesh Kumar Shaily Jain Vivek Bhardwaj Sahil Walia

DOI: https://doi.org/10.5815/ijisa.2025.02.06, Pub. Date: 8 Apr. 2025

Suicide remains a critical global public health issue, claiming vast number of lives each year. Traditional assessment methods, often reliant on subjective evaluations, have limited effectiveness. This study examines the potential of Bidirectional Encoder Representations from Transformers (BERT) in revolutionizing suicide risk prediction by extracting textual biomarkers from relevant data. The research focuses on the efficacy of BERT in classifying suicide-related text data and introduces a novel BERT-based approach that achieves state-of-the-art accuracy, surpassing 97%. These findings highlight BERT's exceptional capability in handling complex text classification tasks, suggesting broad applicability in mental healthcare. The application of Artificial Intelligence (AI) in mental health poses unique challenges, including the absence of established biological markers for suicide risk and the dependence on subjective data, which necessitates careful consideration of potential biases in training datasets. Additionally, ethical considerations surrounding data privacy and responsible AI development are paramount. This study emphasizes the substantial potential of BERT and similar Natural Language Processing (NLP) techniques to significantly improve the accuracy and effectiveness of suicide risk prediction, paving the way for enhanced early detection and intervention strategies. The research acknowledges the inherent limitations of AI-based approaches and stresses the importance of ongoing efforts to address these issues, ensuring ethical and responsible AI application in mental health.

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