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Introduction 2) the AI algorithms for individualized risk prediction of type 2 diabetes mellitus (T2DM) incidence that were constructed and validated with 17-year territory-wide electronic health records (EHR) in Hong Kong; 2) a Chatbot empowered by chatGPT. Results: We developed the clinically explainable prediction models using machine learning (ML) and deep learning (DL) techniques, for two-, five- and ten-year risk predictions in the sub-cohorts of 187,785, 99,853 and 17,301 patients, respectively. We found the Dense Layer Neural Networks (DNN) models achieved the best performance, with the area under receiver-operating curve (AUC) of 81.1%, 79.9%, 75.03% for two-, five- and ten-year risk prediction, respectively. The most significant predictors were fasting glucose and HbA1c, followed by creatinine, age at baseline, sex, potassium, triglyceride, and HDL-C. The DNN models achieved satisfactory performance in external validation. The developed models were then integrated into a web-based prediabetes self-management platform, named DiaLOG, as part of prompt engineering for interaction with ChatGPT. Conclusion: Validated risk prediction models based on EHR data could be integrated into an AI-powered diabetes self-management platform, together with a ChatGPT-powered chatbot, to facilitate clinical decision and empower patients with self-risk assessments and individualized self-management advice. Disclosure J. Lu: None. S. Lu: None. Y. Zhao: None. H. Tsang: None. D. He: None. X. Li: None. L. Yang: None.
LU et al. (Fri,) studied this question.