#clinical-nlp News & Analysis

30 articles tagged with #clinical-nlp. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

30 articles

AINeutralarXiv – CS AI · May 126/10

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WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records

WISTERIA is a machine learning framework that improves clinical AI by treating noisy medical labels as uncertain observations rather than ground truth. By enforcing consistency across multiple weak supervision sources and incorporating medical ontologies, the method achieves better generalization across healthcare institutions and demonstrates robustness to label noise.

AIBullisharXiv – CS AI · May 116/10

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Automated Evaluation can Distinguish the Good and Bad AI Responses to Patient Questions about Hospitalization

Researchers demonstrate that automated evaluation metrics can reliably assess AI-generated responses to patient hospitalization questions, matching human expert ratings across 2,800 responses from 28 AI systems. This approach addresses the scalability limitations of manual expert review while maintaining accuracy across three key dimensions: question answering, clinical evidence use, and medical knowledge application.

AINeutralarXiv – CS AI · May 96/10

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Systematic Evaluation of Large Language Models for Post-Discharge Clinical Action Extraction

Researchers systematically evaluated large language models against supervised BERT models for extracting post-discharge clinical actions from narrative hospital notes. LLMs matched or exceeded supervised baselines on binary actionability detection but lagged on fine-grained multi-label classification, revealing that performance gaps stem from misalignment between model reasoning and annotation conventions rather than pure capability limitations.

AIBullisharXiv – CS AI · Apr 146/10

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Learning Preference-Based Objectives from Clinical Narratives for Sequential Treatment Decision-Making

Researchers propose Clinical Narrative-informed Preference Rewards (CN-PR), a machine learning framework that extracts reward signals from patient discharge summaries to train reinforcement learning models for treatment decisions. The approach achieves strong alignment with clinical outcomes, including improved organ support-free days and faster shock resolution, offering a scalable alternative to traditional reward design in healthcare AI.

AIBullisharXiv – CS AI · Mar 266/10

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PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation

Researchers developed PLACID, a privacy-preserving system using small on-device AI models (2B-10B parameters) for clinical acronym disambiguation in healthcare settings. The cascaded approach combines general-purpose models for detection with domain-specific biomedical models, achieving 81% expansion accuracy while keeping sensitive health data local.

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