Summary of Knowledge Models For Cancer Clinical Practice Guidelines : Construction, Management and Usage in Question Answering, by Pralaypati Ta et al.
Knowledge Models for Cancer Clinical Practice Guidelines : Construction, Management and Usage in Question Answering
by Pralaypati Ta, Bhumika Gupta, Arihant Jain, Sneha Sree C, Keerthi Ram, Mohanasankar Sivaprakasam
First submitted to arxiv on: 23 Jul 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 automated knowledge modeling algorithm transforms Cancer Clinical Practice Guidelines (CPGs) into a programmatically interactable, easy-to-update structured model with minimal human intervention. This improved algorithm addresses the limitations of existing algorithms, which struggle to handle varying complexity in CPGs for different cancer types. The algorithm is evaluated using NCCN CPGs for four different cancer types and demonstrates promising results. Additionally, the paper proposes a Q&A framework utilizing guideline knowledge models as an augmented knowledge base, achieving 54.5% accuracy from treatment algorithms and 81.8% accuracy from discussion parts of the NSCLC guideline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to turn Cancer Clinical Practice Guidelines into a computer-friendly format that can be easily updated without needing much human help. This helps doctors access important information faster. The algorithm is tested using guidelines for four different types of cancer and works well. The team also developed a question-answering system that uses this new format to find answers, which was correct 81.8% of the time. |
Keywords
» Artificial intelligence » Knowledge base » Question answering
