Summary of Emerge: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-augmented Generation, by Yinghao Zhu et al.
EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation
by Yinghao Zhu, Changyu Ren, Zixiang Wang, Xiaochen Zheng, Shiyun Xie, Junlan Feng, Xi Zhu, Zhoujun Li, Liantao Ma, Chengwei Pan
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The integration of multimodal Electronic Health Records (EHR) data has significantly advanced clinical predictive capabilities. Existing models, which utilize clinical notes and multivariate time-series EHR data, often fall short of incorporating the necessary medical context for accurate clinical tasks. EMERGE, a Retrieval-Augmented Generation (RAG) driven framework, aims to enhance multimodal EHR predictive modeling by extracting entities from both time-series data and clinical notes using Large Language Models (LLMs). The extracted knowledge is then used to generate task-relevant summaries of patients’ health statuses. An adaptive multimodal fusion network with cross-attention fuses the summary with other modalities. Experiments on MIMIC-III and MIMIC-IV datasets demonstrate superior performance over baseline models in in-hospital mortality and 30-day readmission tasks. Ablation studies highlight the efficacy of each designed module and robustness to data sparsity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EMERGE is a new way to use Electronic Health Records (EHR) data to make better predictions about patients’ health. It takes clinical notes and time-series EHR data, extracts important information using special language models, and then uses that information to create summaries of patients’ health status. These summaries are then combined with other types of data to make even more accurate predictions. In tests on real patient data, EMERGE did better than other methods in predicting whether patients would die or be readmitted to the hospital within 30 days. |
Keywords
» Artificial intelligence » Cross attention » Rag » Retrieval augmented generation » Time series
