EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models
This addresses the lack of application systems for chronic disease prediction using clinical data, potentially aiding physicians, but it appears incremental as it applies existing LLMM methods to a new EHR dataset.
The researchers tackled the problem of predicting chronic diseases by developing a platform that uses Large Language Multimodal Models (LLMMs) to analyze Electronic Health Records (EHRs) from a Taiwan hospital database, integrating it with web and mobile applications for real-time risk assessment.
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.