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 AI system DIASENTINEL screens diabetes risk using EHRs and ADA guidelines
A new multi-agent system called DIASENTINEL has been developed for on-premise screening of type 2 diabetes risk using electronic health records. This system aims to provide auditable and privacy-preserving clinical deci…
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New EHR risk prediction method uses structured evidence routing
Researchers have developed a novel approach called structured evidence routing for predicting incident risk using longitudinal electronic health records (EHRs). This method employs a router-predictor-reviewer workflow t…
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New multimodal prompt learning framework improves clinical prediction with missing EHR data
Researchers have developed a novel multimodal prompt-learning framework designed to improve the accuracy of clinical predictions from electronic health records (EHRs), particularly when certain data modalities are missi…
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FedCoRe framework tackles missing data in healthcare federated learning
Researchers have developed FedCoRe, a novel framework for federated learning in healthcare that addresses the challenge of missing data modalities. FedCoRe learns to correct for missing information, such as ECGs or ches…
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New Graph Transformer Model Enhances EHR Data Analysis for Clinical Predictions
Researchers have developed MiGHT-EHR, a novel Multi-task Graph Transformer designed to process heterogeneous temporal Electronic Health Records (EHRs). This method constructs a graph where nodes represent clinical entit…
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New LM-GNN Framework Enhances Clinical Prediction with Patient Cohort Insights
Researchers have developed a novel framework called Patients-like-me (PLM) that combines language models (LMs) and graph neural networks (GNNs) for improved clinical prediction using electronic health records (EHRs). Th…
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Healthcare AI adoption hampered by legacy systems and costly niche tools
The healthcare industry is facing a significant challenge with AI adoption, being caught between outdated legacy systems and niche startup solutions. Legacy systems often apply superficial AI features to existing techni…
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Machine learning in surgical risk prediction faces reproducibility and data challenges
A scoping review of 190 studies examining machine learning (ML) approaches for surgical risk stratification and outcome prediction revealed significant methodological gaps. Most studies utilized single-center, private d…
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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…