Evaluating Diabetes Distress Using LLM Digital Twins: A Proof of Concept Case Study
Year: 2026
Type: conference-paper
Abstract: The constant daily management of diabetes often leads to a heavy psychological toll known as diabetes distress, which affects a substantial portion of the patient population and actively worsens blood sugar management. Although the standard Diabetes Distress Scale serves as the primary clinical tool for evaluating this mental strain, limited consultation time and self-reporting inaccuracies severely restrict its practical use.To address this, our research presents an innovative, fully automated clinical support system. By combining Large Language Models and Retrieval-Augmented Generation, the framework constructs a psychological "digital twin" for individual patients. Utilizing the LangGraph architecture, this tool estimates subjective distress levels by evaluating various objective factors, including clinical history, demographics, and lifestyle habits.When elevated psychological risk is identified, the system automatically queries a vectorized repository of major clinical guidelines to generate tailored, evidence-based interventions. By analyzing distinctly different patient profiles - one struggling with significant distress and another showing optimal disease control - the study demonstrates the framework’s possible ability to deliver precisely targeted psychosocial care rather than broad, generic advice.Although currently functioning as a proof-of-concept that requires further validation across broader datasets, this design suggests a potentially scalable method for embedding mental health assessments directly into routine medical visits, ultimately aiding healthcare providers in identifying and treating diabetes distress proactively.
Institution: Obuda University
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7204175681