Summary of Realm: Rag-driven Enhancement Of Multimodal Electronic Health Records Analysis Via Large Language Models, by Yinghao Zhu et al.
REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models
by Yinghao Zhu, Changyu Ren, Shiyun Xie, Shukai Liu, Hangyuan Ji, Zixiang Wang, Tao Sun, Long He, Zhoujun Li, Xi Zhu, Chengwei Pan
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed REALM framework enhances multimodal Electronic Health Records (EHR) representations by leveraging both structured and unstructured data modalities, as well as high-dimensional medical knowledge from a knowledge graph. The approach combines Large Language Model encoding of clinical notes with GRU model-based time-series EHR processing. By prompting the LLM to extract task-relevant medical entities and matching them with professional labels in PrimeKG, the framework eliminates hallucinations and ensures consistency. The adaptive multimodal fusion network integrates extracted knowledge with multimodal EHR data for superior performance on mortality and readmission tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The REALM framework is a new way to analyze Electronic Health Records (EHR) by combining different types of information from these records. It uses special computer models to understand and use the information in the records, which helps doctors make better predictions about patient care. This framework can be used to improve the accuracy of medical diagnoses and treatment plans. |
Keywords
* Artificial intelligence * Knowledge graph * Large language model * Prompting * Time series
