CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908678711410688 |
|---|---|
| author | Dao, Cong-Tinh Phan, Nguyen Minh Thao Ding, Jun-En Wu, Chenwei Restrepo, David Luo, Dongsheng Zhao, Fanyi Liao, Chun-Chieh Peng, Wen-Chih Wang, Chi-Te Chen, Pei-Fu Chen, Ling Ju, Xinglong Liu, Feng Hung, Fang-Ming |
| author_facet | Dao, Cong-Tinh Phan, Nguyen Minh Thao Ding, Jun-En Wu, Chenwei Restrepo, David Luo, Dongsheng Zhao, Fanyi Liao, Chun-Chieh Peng, Wen-Chih Wang, Chi-Te Chen, Pei-Fu Chen, Ling Ju, Xinglong Liu, Feng Hung, Fang-Ming |
| contents | Electronic health records (EHRs) are designed to synthesize diverse data types, including unstructured clinical notes, structured lab tests, and time-series visit data. Physicians draw on these multimodal and temporal sources of EHR data to form a comprehensive view of a patient's health, which is crucial for informed therapeutic decision-making. Yet, most predictive models fail to fully capture the interactions, redundancies, and temporal patterns across multiple data modalities, often focusing on a single data type or overlooking these complexities. In this paper, we present CURENet, a multimodal model (Combining Unified Representations for Efficient chronic disease prediction) that integrates unstructured clinical notes, lab tests, and patients' time-series data by utilizing large language models (LLMs) for clinical text processing and textual lab tests, as well as transformer encoders for longitudinal sequential visits. CURENet has been capable of capturing the intricate interaction between different forms of clinical data and creating a more reliable predictive model for chronic illnesses. We evaluated CURENet using the public MIMIC-III and private FEMH datasets, where it achieved over 94\% accuracy in predicting the top 10 chronic conditions in a multi-label framework. Our findings highlight the potential of multimodal EHR integration to enhance clinical decision-making and improve patient outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11423 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction Dao, Cong-Tinh Phan, Nguyen Minh Thao Ding, Jun-En Wu, Chenwei Restrepo, David Luo, Dongsheng Zhao, Fanyi Liao, Chun-Chieh Peng, Wen-Chih Wang, Chi-Te Chen, Pei-Fu Chen, Ling Ju, Xinglong Liu, Feng Hung, Fang-Ming Artificial Intelligence Electronic health records (EHRs) are designed to synthesize diverse data types, including unstructured clinical notes, structured lab tests, and time-series visit data. Physicians draw on these multimodal and temporal sources of EHR data to form a comprehensive view of a patient's health, which is crucial for informed therapeutic decision-making. Yet, most predictive models fail to fully capture the interactions, redundancies, and temporal patterns across multiple data modalities, often focusing on a single data type or overlooking these complexities. In this paper, we present CURENet, a multimodal model (Combining Unified Representations for Efficient chronic disease prediction) that integrates unstructured clinical notes, lab tests, and patients' time-series data by utilizing large language models (LLMs) for clinical text processing and textual lab tests, as well as transformer encoders for longitudinal sequential visits. CURENet has been capable of capturing the intricate interaction between different forms of clinical data and creating a more reliable predictive model for chronic illnesses. We evaluated CURENet using the public MIMIC-III and private FEMH datasets, where it achieved over 94\% accuracy in predicting the top 10 chronic conditions in a multi-label framework. Our findings highlight the potential of multimodal EHR integration to enhance clinical decision-making and improve patient outcomes. |
| title | CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.11423 |