Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses
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View abstract on PubMed
Summary
This summary is machine-generated.Automated metrics struggle to evaluate clinical Natural Language Generation (NLG) quality. Domain-specific knowledge, like the SapBERT score, is crucial for accurate healthcare text assessment.
Area Of Science
- Medical Informatics
- Natural Language Processing
Background
- Assessing clinical Natural Language Generation (NLG) text quality is difficult.
- Existing automated metrics often fail to capture the complexities of generative tasks in healthcare.
Approach
- Developed a comprehensive human evaluation framework to establish a validated baseline.
- Correlated human judgments with various automated metrics using ChatGPT-3.5-turbo generated output.
Key Points
- No automated metrics showed high alignment with human judgments.
- The SapBERT score, leveraging the Unified Medical Language System (UMLS), demonstrated the best performance.
- Domain-specific knowledge integration is vital for effective NLG evaluation.
Conclusions
- Current automated evaluation metrics for clinical NLG are deficient.
- The proposed human evaluation framework serves as a robust baseline.
- Future work should focus on integrating medical knowledge databases to refine metrics like SapBERT for better accuracy.
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