3 articles tagged with #electronic-health-records. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce CBR-to-SQL, a new framework using Case-Based Reasoning to improve natural language-to-SQL translation for healthcare databases. The system addresses limitations of standard RAG approaches by using two-stage retrieval and abstract case templates, achieving state-of-the-art results on medical datasets.
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.
Researchers developed TS4NAP, an AI approach that uses medical taxonomies and graph matching to predict next treatment steps for patients. The method leverages domain-specific knowledge from ICD-10 medical codes to improve treatment planning recommendations and make predictions more explainable for physicians.
