#electronic-health-records News & Analysis

4 articles tagged with #electronic-health-records. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · 6d ago6/10

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Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints

Researchers present AWARE, a retrieval-aligned framework for improving clinical risk prediction in electronic health records using tabular foundation models. The method addresses limitations of naive retrieval-augmented approaches in clinical settings, achieving up to 12.2% improvement in AUPRC under extreme class imbalance while maintaining robustness across varying data complexity.

AIBullisharXiv – CS AI · Mar 96/10

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CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain

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.

AIBullisharXiv – CS AI · Mar 36/103

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Knowledge Graph Augmented Large Language Models for Disease Prediction

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.

AINeutralarXiv – CS AI · Mar 54/10

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Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment

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.