Traditional diagnosis of chronic diseases involves in-person consultations with physicians to identify the disease. However, there is a lack of research focused on predicting and developing application systems using clinical notes and blood test values. We collected five years of Electronic Health Records (EHRs) from Taiwan's hospital database between 2017 and 2021 as an AI database. Furthermore, we developed an EHR-based chronic disease prediction platform utilizing Large Language Multimodal Models (LLMMs), successfully integrating with frontend web and mobile applications for prediction. This prediction platform can also connect to the hospital's backend database, providing physicians with real-time risk assessment diagnostics. The demonstration link can be found at https://www.youtube.com/watch?v=oqmL9DEDFgA.

翻译：慢性疾病的传统诊断需通过医生面诊以确定病症。然而，目前缺乏利用临床记录与血液检测值进行预测并开发应用系统的研究。我们从台湾医院数据库中收集了2017年至2021年间的五年电子健康记录（EHRs）作为人工智能数据库。进一步，我们开发了一个基于EHR的慢性病预测平台，该平台利用大型语言多模态模型（LLMMs），并成功与前端网络及移动应用程序集成以实现预测功能。此预测平台还能连接医院的后端数据库，为医生提供实时风险评估诊断。演示链接可见：https://www.youtube.com/watch?v=oqmL9DEDFgA。