IJISA Vol. 17, No. 2, 8 Apr. 2025
Cover page and Table of Contents: PDF (size: 504KB)
PDF (504KB), PP.101-111
Views: 0 Downloads: 0
Artificial Intelligence, Suicide Prevention, Depression Detection, Machine Learning, Mental Health, Bidirectional Encoder Representations from Transformers, Textual Biomarkers, Natural Language Processing
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.
Karan Bajaj, Mukesh Kumar, Shaily Jain, Vivek Bhardwaj, Sahil Walia, "Enhancing Suicide Risk Prediction through BERT: Leveraging Textual Biomarkers for Early Detection", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.2, pp.101-111, 2025. DOI:10.5815/ijisa.2025.02.06
[1]Martínez-Castaño, R., Htait, A., Azzopardi, L., & Moshfeghi, Y. (2021). BERT-based transformers for early detection of mental health illnesses. In Experimental IR Meets Multilinguality, Multimodality, and Interaction: 12th International Conference of the CLEF Association, CLEF 2021, Virtual Event, September 21–24, 2021, Proceedings 12 (pp. 189-200). Springer International Publishing.
[2]Grimland, M., Benatov, J., Yeshayahu, H., Izmaylov, D., Segal, A., Gal, K., & Levi‐Belz, Y. (2024). Predicting suicide risk in real‐time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box. Suicide and Life‐Threatening Behavior.
[3]Jacobucci, R., Ammerman, B. A., & Tyler Wilcox, K. (2021). The use of text‐based responses to improve our understanding and prediction of suicide risk. Suicide and Life‐Threatening Behavior, 51(1), 55-64.
[4]Fonseka, T. M., Bhat, V., & Kennedy, S. H. (2019). The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors. Australian & New Zealand Journal of Psychiatry, 53(10), 954-964.
[5]Rosenfeld, A., Benrimoh, D., Armstrong, C., Mirchi, N., Langlois-Therrien, T., Rollins, C., ... & Yaniv-Rosenfeld, A. (2021). Big Data analytics and artificial intelligence in mental healthcare. In Applications of big data in healthcare (pp. 137-171). Academic Press.
[6]Sweeney, C., Ennis, E., Mulvenna, M. D., Bond, R., & O'Neill, S. (2024). Insights derived from text-based digital media, in relation to mental health and suicide prevention, using data analysis and machine learning: systematic review. JMIR mental health, 11, e55747.
[7]Burkhardt, H. A., Ding, X., Kerbrat, A., Comtois, K. A., & Cohen, T. (2023). From benchmark to bedside: transfer learning from social media to patient-provider text messages for suicide risk prediction. Journal of the American Medical Informatics Association, 30(6), 1068-1078.
[8]Ophir, Y., Tikochinski, R., Asterhan, C. S., Sisso, I., & Reichart, R. (2020). Deep neural networks detect suicide risk from textual facebook posts. Scientific reports, 10(1), 16685.
[9]Malgaroli, M., Hull, T. D., Bantilan, N., Ray, B., & Simon, N. (2020). Suicide Risk Automated Detection Using Computational Linguistic Markers from Patients’ Communication With Therapists. Biological psychiatry, 87(9), S444.
[10]Levis, M., Levy, J., Dufort, V., Gobbel, G. T., Watts, B. V., & Shiner, B. (2022). Leveraging unstructured electronic medical record notes to derive population-specific suicide risk models. Psychiatry research, 315, 114703.
[11]Poulin, C., Shiner, B., Thompson, P., Vepstas, L., Young-Xu, Y., Goertzel, B., ... & McAllister, T. (2014). Predicting the risk of suicide by analyzing the text of clinical notes. PloS one, 9(1), e85733.
[12]Naseem, U., Khushi, M., Kim, J., & Dunn, A. G. (2022). Hybrid text representation for explainable suicide risk identification on social media. IEEE transactions on computational social systems.
[13]Baghdadi, N. A., Malki, A., Balaha, H. M., AbdulAzeem, Y., Badawy, M., & Elhosseini, M. (2022). An optimized deep learning approach for suicide detection through Arabic tweets. PeerJ Computer Science, 8, e1070.
[14]Cohen, J., Wright-Berryman, J., Rohlfs, L., Trocinski, D., Daniel, L., & Klatt, T. W. (2022). Integration and validation of a natural language processing machine learning suicide risk prediction model based on open-ended interview language in the emergency department. Frontiers in digital health, 4, 818705.
[15]Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2017). Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science, 5(3), 457-469.
[16]Levis, M., Westgate, C. L., Gui, J., Watts, B. V., & Shiner, B. (2021). Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models. Psychological medicine, 51(8), 1382-1391.
[17]Barak-Corren, Y., Castro, V. M., Nock, M. K., Mandl, K. D., Madsen, E. M., Seiger, A., ... & Smoller, J. W. (2020). Validation of an electronic health record–based suicide risk prediction modeling approach across multiple health care systems. JAMA network open, 3(3), e201262-e201262.
[18]Ji, S., Li, X., Huang, Z., & Cambria, E. (2022). Suicidal ideation and mental disorder detection with attentive relation networks. Neural Computing and Applications, 34(13), 10309-10319.
[19]Kumari, M., Singh, G., & Pande, S. D. (2024). Depressonify: BERT a deep learning approach of detection of depression. EAI Endorsed Transactions on Pervasive Health and Technology, 10.
[20]Sawhney, R., Joshi, H., Gandhi, S., Jin, D., & Shah, R. R. (2021). Robust suicide risk assessment on social media via deep adversarial learning. Journal of the American Medical Informatics Association, 28(7), 1497-1506.
[21]McCoy Jr, T. H., Pellegrini, A. M., & Perlis, R. H. (2019). Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death. Depression and anxiety, 36(5), 392-399.
[22]Arowosegbe, A., & Oyelade, T. (2023). Application of natural language processing (NLP) in detecting and preventing suicide ideation: a systematic review. International Journal of Environmental Research and Public Health, 20(2), 1514.
[23]Zheng, L., Wang, O., Hao, S., Ye, C., Liu, M., Xia, M., ... & Ling, X. B. (2020). Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. Translational psychiatry, 10(1), 72.
[24]Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019). Detection of suicide ideation in social media forums using deep learning. Algorithms, 13(1), 7.
[25]Tsui, F. R., Shi, L., Ruiz, V., Ryan, N. D., Biernesser, C., Iyengar, S., ... & Brent, D. A. (2021). Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts. JAMIA open, 4(1), ooab011.
[26]Velupillai, S., Hadlaczky, G., Baca-Garcia, E., Gorrell, G. M., Werbeloff, N., Nguyen, D., ... & Dutta, R. (2019). Risk assessment tools and data-driven approaches for predicting and preventing suicidal behavior. Frontiers in psychiatry, 10, 36.
[27]Moradian, H., Lau, M. A., Miki, A., Klonsky, E. D., & Chapman, A. L. (2023). Identifying suicide ideation in mental health application posts: A random forest algorithm. Death Studies, 47(9), 1044-1052.
[28]Bayramli, I., Castro, V., Barak-Corren, Y., Madsen, E. M., Nock, M. K., Smoller, J. W., & Reis, B. Y. (2022). Temporally informed random forests for suicide risk prediction. Journal of the American Medical Informatics Association, 29(1), 62-71.
[29]Howard, D., Maslej, M. M., Lee, J., Ritchie, J., Woollard, G., & French, L. (2020). Transfer learning for risk classification of social media posts: model evaluation study. Journal of medical Internet research, 22(5), e15371.
[30]Burke, T. A., Ammerman, B. A., & Jacobucci, R. (2019). The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review. Journal of affective disorders, 245, 869-884.
[31]Suicide and Depression Detection Dataset: https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch/data Accessed on :15/06/2024.