Summary of Multimodal Fusion Of Ehr in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and Llm, by Hejie Cui et al.
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
by Hejie Cui, Xinyu Fang, Ran Xu, Xuan Kan, Joyce C. Ho, Carl Yang
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 A novel framework called MINGLE is proposed to integrate both structured and unstructured Electronic Health Records (EHRs) effectively. The framework uses a two-level infusion strategy to combine medical concept semantics and clinical note semantics into hypergraph neural networks, which learn the complex interactions between different types of data to generate visit representations for downstream prediction. This multimodal fusion approach improves predictive performance by 11.83% relatively on two EHR datasets, enhancing semantic integration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EHRs are like digital files that doctors use to make decisions about patients’ health. These files often contain different kinds of information, like tables and notes. Researchers have been working on ways to understand this information better. In this project, scientists developed a new way to combine these different types of data called MINGLE. It works by using special computer models to analyze the relationships between different parts of the EHRs. The results show that MINGLE can help doctors make more accurate predictions about patients’ health. |
Keywords
* Artificial intelligence * Semantics
