基于电子健康记录的大型语言多模态模型的慢性病风险预测移动与网络平台
EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models
AI总结：
本研究利用台湾医院五年电子健康记录，构建基于大型语言多模态模型的慢性病预测平台，集成网络与移动应用，提供实时风险评估。
摘要：
慢性病的传统诊断方式涉及与医生进行面对面咨询以识别疾病。然而，目前缺乏利用临床笔记和血液检测值进行预测和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年间五年的电子健康记录（EHRs）作为AI数据库。此外，我们开发了一个基于EHR的慢性病预测平台，利用大型语言多模态模型（LLMMs），成功与前端网络和移动应用集成以进行预测。该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。演示链接可在https://www.youtube.com/watch?v=oqmL9DEDFgA找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
