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

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 ۲۰۱۸ to ۲۰۲۳ 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