EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913405532635136 |
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| author | Liao, Chun-Chieh Kuo, Wei-Ting Hu, I-Hsuan Shih, Yen-Chen Ding, Jun-En Liu, Feng Hung, Fang-Ming |
| author_facet | Liao, Chun-Chieh Kuo, Wei-Ting Hu, I-Hsuan Shih, Yen-Chen Ding, Jun-En Liu, Feng Hung, Fang-Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18087 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models Liao, Chun-Chieh Kuo, Wei-Ting Hu, I-Hsuan Shih, Yen-Chen Ding, Jun-En Liu, Feng Hung, Fang-Ming Software Engineering Artificial Intelligence Computation and Language 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. |
| title | EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models |
| topic | Software Engineering Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2406.18087 |