#clinical-prediction News & Analysis

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

4 articles

AIBullisharXiv – CS AI · May 127/10

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Event Fields: Learning Latent Event Structure for Waveform Foundation Models

Researchers introduce a novel waveform foundation model that represents physiological signals as latent event processes rather than sequential tokens, using self-supervised learning to capture clinically meaningful structure. The approach demonstrates improved performance on medical benchmarks including arrhythmia classification and hemodynamic prediction, suggesting event-centric representations may be more suitable for healthcare AI than traditional sequence-based methods.

AIBullisharXiv – CS AI · Mar 46/103

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MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

Researchers introduce MedFeat, a new AI framework that uses Large Language Models for healthcare feature engineering in clinical tabular predictions. The system incorporates model awareness and domain knowledge to discover clinically meaningful features that outperform traditional approaches and demonstrate robustness across different hospital settings.

AINeutralarXiv – CS AI · 6d ago6/10

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Prospective evaluation of multimodal respiratory failure prediction: Do chest X-rays improve performance beyond EHR signals?

Researchers developed a gated multimodal AI framework that combines electronic health record data with chest X-ray analysis to predict respiratory failure in ICU patients within 24 hours. The model achieved significantly higher accuracy (AUROC 0.860) than EHR-only baselines and physician predictions, demonstrating that adaptive fusion of imaging and structured clinical data improves critical care decision-making.

AINeutralarXiv – CS AI · May 126/10

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AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks

Researchers benchmarked LLM-based agents for multimodal clinical prediction tasks using real-world healthcare data, finding that single-agent systems outperform naive multi-agent frameworks in handling diverse data types like medical images, notes, and EHR records. The study reveals critical limitations in current multi-agent collaboration approaches and provides an open-source evaluation framework to advance clinical AI development.