Ehrs
PulseAugur coverage of Ehrs — every cluster mentioning Ehrs across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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New framework integrates multimodal clinical data for EHR foundation models
Researchers have developed a new framework for autoregressive foundation models that can process multimodal clinical data, including ECG waveforms, chest X-ray images, and clinical notes, alongside structured electronic…
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AI and Real-World Evidence Accelerate Rare Disease Treatment Development
Artificial intelligence (AI) and real-world evidence (RWE) are converging to accelerate the development of treatments for rare diseases. Traditional clinical trials are often impractical for these conditions due to smal…
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VISTA Architect AI system integrates LLMs with EHRs for medical data synthesis
Researchers have developed VISTA Architect, a novel AI system designed to integrate large language models with electronic health records (EHRs). This system transforms clinical data into a knowledge graph, creating a sy…
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New MedTPE method compresses EHR data for LLMs with no performance loss
Researchers have developed a new method called Medical Token-Pair Encoding (MedTPE) to efficiently compress long electronic health record sequences for large language models. This technique merges frequently occurring m…
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AI models predict patient risk using clinical notes and temporal data
Researchers have developed two novel methods, HiTGNN and ReVeAL, to improve early risk prediction for chronic diseases using clinical language processing. HiTGNN, a hierarchical temporal graph neural network, effectivel…
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LLMs and normalizing flows tackle incomplete healthcare data for treatment effect estimation
Researchers have developed a novel two-stage pipeline, CausalFlow-T, designed to improve treatment effect estimation from incomplete longitudinal electronic health records. The first stage utilizes a DAG-constrained nor…
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Study: Shorter data windows optimize AI for hospital readmission prediction
A new study published on arXiv explores the optimal historical data window for predicting hospital readmissions. Researchers found that for unstructured clinical notes, a shorter window of three to six months prior to s…
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Sparse Autoencoder Decomposition of Clinical Sequence Model Representations: Feature Complexity, Task Specialisation, and Mortality Prediction
Researchers have developed several novel approaches to improve clinical prediction using machine learning on electronic health records (EHRs). One method, Risk Horizons, uses a geometry-aware framework with hyperbolic e…