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🧠 AI🟢 BullishImportance 6/10
Knowledge Graph Augmented Large Language Models for Disease Prediction
arXiv – CS AI|Ruiyu Wang, Tuan Vinh, Ran Xu, Yuyin Zhou, Jiaying Lu, Carl Yang, Francisco Pasquel||3 views
🤖AI Summary
Researchers developed a knowledge graph-guided chain-of-thought framework that uses large language models for disease prediction from electronic health records. The approach outperformed classical baselines and showed strong zero-shot transfer capabilities, with clinicians preferring the AI-generated explanations for their clarity and relevance.
Key Takeaways
- →Knowledge graph-guided LLMs achieved AUROC scores of 0.66-0.70 for disease prediction using only 400-1,000 training samples.
- →The models demonstrated strong zero-shot transfer capabilities, improving accuracy from 0.40-0.51 to 0.72-0.77 on new datasets.
- →Clinicians preferred the AI-generated explanations over traditional methods for clarity, relevance, and correctness in blinded studies.
- →The framework uses lightweight 7B-8B parameter models, making it more accessible for healthcare applications.
- →The approach maps medical codes to knowledge graphs to generate temporally consistent reasoning chains.
#artificial-intelligence#healthcare-ai#large-language-models#knowledge-graphs#medical-prediction#electronic-health-records#chain-of-thought#zero-shot-learning
Read Original →via arXiv – CS AI
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