Summary of Dualmar: Medical-augmented Representation From Dual-expertise Perspectives, by Pengfei Hu et al.
DualMAR: Medical-Augmented Representation from Dual-Expertise Perspectives
by Pengfei Hu, Chang Lu, Fei Wang, Yue Ning
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 DualMAR is proposed to enhance Electronic Health Records (EHR) prediction tasks by incorporating both individual observation data and public knowledge bases. The framework constructs a bi-hierarchical Diagnosis Knowledge Graph (KG) using verified public clinical ontologies, which is then augmented via Large Language Models (LLMs). Additionally, a new proxy-task learning approach is designed for pretraining on lab results in EHR to further enhance KG representation and patient embeddings. DualMAR enables accurate predictions based on rich hierarchical and semantic embeddings from KG. Experimental results demonstrate that DualMAR outperforms state-of-the-art models, validating its effectiveness in EHR prediction and KG integration in medical domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new AI framework called DualMAR helps predict diagnoses and treatments by combining patient data with publicly available knowledge. This approach creates a special kind of map called a Diagnosis Knowledge Graph (KG) that combines information from verified sources. The KG is then improved using large language models. Another innovation is a training method that uses lab test results to enhance the KG’s ability to represent patients and diagnoses. DualMAR makes more accurate predictions by using these enhanced representations. The results show that DualMAR outperforms other approaches, making it a valuable tool for healthcare prediction. |
Keywords
» Artificial intelligence » Knowledge graph » Pretraining
