Summary of A Knowledge-enhanced Disease Diagnosis Method Based on Prompt Learning and Bert Integration, by Zhang Zheng
A Knowledge-Enhanced Disease Diagnosis Method Based on Prompt Learning and BERT Integration
by Zhang Zheng
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 knowledge-enhanced disease diagnosis method uses a prompt learning framework to retrieve structured knowledge from external graphs related to clinical cases. This knowledge is then encoded and injected into language model templates to enhance understanding and reasoning capabilities for diagnosing diseases. The method was tested on three public datasets (CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR) and outperformed existing models across multiple evaluation metrics, with significant improvements in F1 scores. Ablation studies confirmed the critical role of the knowledge injection module, demonstrating that the proposed method not only improves accuracy but also enhances interpretability, providing reliable support for clinical diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a disease diagnosis tool better by adding information from external sources. It uses a special framework to combine this information with a language model to help doctors make more accurate diagnoses. The test results show that the new method is better than old ones and can even explain why it’s making certain diagnoses, which is helpful for doctors. |
Keywords
» Artificial intelligence » Language model » Ner » Prompt
