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MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models

Autor: Phan, Thao Minh Nguyen, Dao, Cong-Tinh, Wu, Chenwei, Wang, Jian-Zhe, Liu, Shun, Ding, Jun-En, Restrepo, David, Liu, Feng, Hung, Fang-Ming, Peng, Wen-Chih

Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes. In real-life clinical practice, doctors use complementary multimodal EHR data sources to get a cleare

Externí odkaz: http://arxiv.org/abs/2407.12309

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Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data

Autor: Ding, Jun-En, Thao, Phan Nguyen Minh, Peng, Wen-Chih, Wang, Jian-Zhe, Chug, Chun-Cheng, Hsieh, Min-Chen, Tseng, Yun-Chien, Chen, Ling, Luo, Dongsheng, Wang, Chi-Te, Chen, Pei-fu, Liu, Feng, Hung, Fang-Ming

Chronic diseases such as diabetes are the leading causes of morbidity and mortality worldwide. Numerous research studies have been attempted with various deep learning models in diagnosis. However, most previous studies had certain limitations, inclu

Externí odkaz: http://arxiv.org/abs/2403.04785

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Akademický článek

Large language multimodal models for new-onset type 2 diabetes prediction using five-year cohort electronic health records

Autor: Jun-En Ding, Phan Nguyen Minh Thao, Wen-Chih Peng, Jian-Zhe Wang, Chun-Cheng Chug, Min-Chen Hsieh, Yun-Chien Tseng, Ling Chen, Dongsheng Luo, Chenwei Wu, Chi-Te Wang, Chih-Ho Hsu, Yi-Tui Chen, Pei-Fu Chen, Feng Liu, Fang-Ming Hung

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)

Abstract Type 2 diabetes mellitus (T2DM) is a prevalent health challenge faced by countries worldwide. In this study, we propose a novel large language multimodal models (LLMMs) framework incorporating multimodal data from clinical notes and laborato

Externí odkaz: https://doaj.org/article/b2ceacc8b2fc4af681bf63706937197d