Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

Diagnosing Death Anxiety with AI in Diabetics and Phobias and Distraught Dreams

Document Type : Original Article

Authors
M. Khadempir 1
R. Rahimi 2
P. Hamidi 3
E. Ghaderi 4
M. Hoghooghi 5
1 Department of Sports Pathology and Corrective Movements, Farhangian University, Mashhad, Iran
2 Department of Elementary Sciences, Faculty of Elementary Sciences, Farhangian University, Mashhad, Iran
3 Department of Counseling and Guidance, Faculty of Science, Islamic Azad University of Qochan Branch, Qochan, Iran
4 Department of Psychology, Faculty of Psychology, Payam Noor University, Chenaran, Iran
5 Department of Business Administration, Faculty of Management, Payam Noor University, Chenaran, Iran
10.47176/TDASS.2024.1
Abstract
Artificial intelligence (AI) offers significant potential for detecting symptoms of death anxiety and improving both diagnostic accuracy and therapeutic strategies. This study seeks to provide healthcare professionals with more efficient tools to manage death anxiety in individuals with diabetes, phobias, and sleep disorders. While mental health concerns have traditionally been explored within social frameworks, AI is increasingly recognized as a valuable resource in psychological care. This paper presents an integrative analysis of the relationship between diabetes, phobias, sleep disturbances, and death anxiety, focusing on research conducted from 2018 to 2023 using Latent Dirichlet Allocation (LDA) thematic modeling. Findings suggest that investigating the overlap of these conditions may offer meaningful insights for future studies. Importantly, AI appears to enhance the early identification and treatment of anxiety, particularly anxiety related to mortality. By analyzing sensor data with AI algorithms, early indicators of anxiety can be detected, allowing timely intervention and improved patient outcomes. The study utilized the Web of Science database, applying search terms such as “diabetes,” “phobia,” and “sleep disorders.” The LDA model revealed hidden semantic structures and calculated co-occurrence metrics to evaluate thematic coherence. Overall, this research highlights AI’s critical role in the detection and management of death anxiety and emphasizes the need for continued investigation in this domain.
Keywords
Phobia
Artificial Intelligence
Death Anxiety
Sleep Disorders
Diabetes
LDA Thematic Modeling
  • Kulmanov, M., & Hoehndorf, R. (2020). DeepPheno: predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier. PLoS Computational Biology, 16(11), e1008453. https://doi.org/10.1371/journal.pcbi.1008453
  • Lello, L., Raben, T. G., Yong, S. Y., Tellier, L. C. A. M., & Hsu, S. D. H. (2019). Genomic prediction of 16 complex disease risks including heart attack, diabetes, breast and prostate cancer. Scientific Reports, 9(1), 15286. https://doi.org/10.1038/s41598-019-51258-x
  • Murphy, K., et al. (2021). Artificial intelligence for good health: A scoping review of the ethics literature. BMC Medical Ethics, 22(1), 14. https://doi.org/10.1186/s12910-021-00577-8
  • Bolton, W. J., et al. (2022). Developing moral AI to support antimicrobial decision making. Nature Machine Intelligence. Preprint available at https://doi.org/10.48550/arXiv.2208.06327. https://doi.org/10.1038/s42256-022-00558-5
  • Martinez-Martin, N., et al. (2021). Ethical issues in using ambient intelligence in healthcare settings. The Lancet Digital Health, 3(2), e115-e123. https://doi.org/10.1016/S2589-7500(20)30275-2
  • Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare. https://doi.org/10.1016/B978-0-12-818438-7.00012-5
  • World Health Organization. (n.d.). Ethics and governance of artificial intelligence for health. Retrieved from https://www.who.int/publications-detail-redirect/9789240029200
  • Badea, C., & Gilpin, L. (2021). Establishing meta-decision-making for AI: An ontology of relevance, representation and reasoning. In AAAI 2021 Fall Symposium FSS-21. Also available at: https://doi.org/10.48550/arXiv.2210.00608
  • Smith, S. S., Kitterick, P. T., Scutt, P., Baguley, D. M., & Pierzycki, R. H. (2021). An exploration of psychological symptom-based phenotyping of adult cochlear implant users with and without tinnitus using a machine learning approach. Progress in Brain Research, 260, 283-300. https://doi.org/10.1016/bs.pbr.2020.10.002
  • Jacobson, N. C., Lekkas, D., Huang, R., & Thomas, N. (2021). Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17-18 years. Journal of Affective Disorders, 282, 104-111. https://doi.org/10.1016/j.jad.2020.12.086
  • Mohr, D., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23-47. https://doi.org/10.1146/annurev-clinpsy-032816-044949
  • Torous, J., Wisniewski, H., Bird, B., Carpenter, E., David, G., Elejalde, E., et al. (2019). Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: An interdisciplinary and collaborative approach. Journal of Technology in Behavioral Science, 4, 73-85. https://doi.org/10.1007/s41347-019-00095-w
  • Torous, J., & Walker, R. (2019). Leveraging digital health and machine learning toward reducing suicide from panacea to practical tool. JAMA Psychiatry, 76, 999-1000. https://doi.org/10.1001/jamapsychiatry.2019.1231
  • Ben-Zeev, D., Brian, R., Wang, R., Wang, W., Campbell, A. T., Aung, M. S. H., et al. (2017). CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse. Psychiatric Rehabilitation Journal, 40, 266. https://doi.org/10.1037/prj0000243
  • Doryab, A., Villalba, D. K., Chikersal, P., Dutcher, J. M., Tumminia, M., Liu, X., et al. (2019). Identifying behavioral phenotypes of loneliness and social isolation with passive sensing: Statistical analysis, data mining, and machine learning of smartphone and Fitbit data. JMIR mHealth and uHealth, 7, e13209. https://doi.org/10.2196/13209
  • Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20, 531-558. https://doi.org/10.1007/s10796-017-9810-y
  • De Choudhury, M., Counts, S., & Gamon, M. (2012). Not all moods are created equal: Exploring human emotional states in social media. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media, 6(1), 66-73. https://doi.org/10.1609/icwsm.v6i1.14279
  • De Choudhury, M., Counts, S., & Gamon, M. (2012). Happy, nervous or surprised? Classification of human affective states in social media. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media, 6(1), 66-73.
  • Birnbaum, M. L., Rizvi, A. F., Correll, C. U., Kane, J. M., & Confino, J. (2017). Role of social media and the internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders. Early Intervention in Psychiatry, 11, 290-295. https://doi.org/10.1111/eip.12237
  • Birnbaum, M. L., Ernala, S. K., Rizvi, A. F., Arenare, E., Van Meter, A. R., De Choudhury, M., & Kane, J. M. (2019). Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook. npj Schizophrenia, 5, 1-9. https://doi.org/10.1038/s41537-019-0085-9
  • De Choudhury, M., & De, S. (2014). Mental health discourse on Reddit: Self-disclosure, social support, and anonymity. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, 8(1), 71-80. https://doi.org/10.1609/icwsm.v8i1.14526
  • Jojoa, M., Lazaro, E., Garcia-Zapirain, B., Gonzalez, M. J., & Urizar, E. (2021). The impact of COVID-19 on university staff and students from Iberoamerica: Online learning and teaching experience. International Journal of Environmental Research and Public Health, 18(11), 5820. https://doi.org/10.3390/ijerph18115820
  • Linden, T., De Jong, J., Lu, C., Kiri, V., Haeffs, K., & Fröhlich, H. (2021). An explainable multimodal neural network architecture for predicting epilepsy comorbidities based on administrative claims data. Frontiers in Artificial Intelligence, 4, Article 610197. https://doi.org/10.3389/frai.2021.610197
  • Bhat, S., Acharya, U. R., Hagiwara, Y., Dadmehr, N., & Adeli, H. (2018). Parkinson's disease: Cause factors, measurable indicators, and early diagnosis. Computers in Biology and Medicine, 102, 234-241. https://doi.org/10.1016/j.compbiomed.2018.09.008
Transactions on Data Analysis in Social Science
Volume 6, Issue 1
Winter 2024
Pages 1-10

  • Receive Date 10 November 2023
  • Revise Date 15 January 2024
  • Accept Date 12 February 2024