State Required CME

A Longitudinal Clinical Foundation Model on Nationwide Veteran Health Trajectories

We present VA-LLM, a 1.62-billion-parameter autoregressive transformer pre-trained from scratch on 1.74 trillion tokens of clinical text spanning 22 years of care for 13.8 million patients in the Veterans Health Administration, with mortality outcomes confirmed through the National Death Index for 7.8 million patients. In a retrospective-prospective evaluation on 107,555 withheld patients, VA-LLM ...

Unlocking Multi-Site Clinical Data: A Federated Approach to Privacy-First Child Autism Behavior Analysis

Automated recognition of autistic behaviors in children is essential for early intervention and objective clinical assessment. However, the development of robust models is severely hindered by strict privacy regulations (e.g., HIPAA) and the sensitive nature of pediatric data, which prevents the centralized aggregation of clinical datasets. Furthermore, individual clinical sites often suffer from ...

Fully Automated Abstraction of Longitudinal Breast Oncology Records with Off-The-Shelf Large Language Models

Background: Manual chart abstraction is a major bottleneck in clinical research. In oncology, important outcomes such as disease recurrence and the tr...

Multi-Criteria Validation of LLM-Inferred Depression Severity from Outpatient Psychiatry Notes

Background: Longitudinal measurement of depression severity in outpatient psychiatric care is limited by infrequent standardized assessments. Although...

Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation

Reinforcement learning (RL) has emerged as a promising paradigm for enhancing image editing and text-to-image (T2I) generation. However, current rewar...

Semantic Risk Scoring of Aggregated Metrics: An AI-Driven Approach for Healthcare Data Governance

Large healthcare institutions typically operate multiple business intelligence (BI) teams segmented by domain, including clinical performance, fundrai...

A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments

Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, imp...

Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials

The digitization of healthcare has generated massive volumes of Electronic Health Records (EHRs), offering unprecedented opportunities for training Ar...

Hybrid rule-based and on-premises LLM pipeline for extracting CMR and CPET metrics from free-text reports in repaired tetralogy of Fallot

Background Patients with repaired tetralogy of Fallot (rTOF) require lifelong surveillance with cardiovascular magnetic resonance (CMR) and cardiopulm...

Federated Proximal Optimization for Privacy-Preserving Heart Disease Prediction: A Controlled Simulation Study on Non-IID Clinical Data

Healthcare institutions have access to valuable patient data that could be of great help in the development of improved diagnostic models, but privacy...

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning

Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple...

Federated Learning for Predictive Analytics in Weaning from Mechanical Ventilation.

Mechanical ventilation is crucial for critically ill patients in ICUs, requiring accurate weaning and extubations timing for optimal outcomes. Current...

KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification

The increasing volume of healthcare textual data requires computationally efficient, yet highly accurate classification approaches able to handle th...

CMEdataset Advancing China Map Detection and Standardization with Digital Image Resources

Digital images of Chinas maps play a crucial role in map detection, particularly in ensuring national sovereignty, territorial integrity, and map co...

Beacon2Science: Enhancing STEREO/HI beacon data1 with machine learning for efficient CME tracking

Observing and forecasting coronal mass ejections (CME) in real-time is crucial due to the strong geomagnetic storms they can generate that can have ...

Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model

We present a transformer model, named DeepHalo, to predict the occurrence of halo coronal mass ejections (CMEs). Our model takes as input an active ...

Development of secure infrastructure for advancing generative artificial intelligence research in healthcare at an academic medical center.

BACKGROUND: Generative AI, particularly large language models (LLMs), holds great potential for improving patient care and operational efficiency in h...

FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework

With the increasing prevalence of mental health conditions worldwide, AI-powered chatbots and conversational agents have emerged as accessible tools...

Implications of Artificial Intelligence on Health Data Privacy and Confidentiality

The rapid integration of artificial intelligence (AI) in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational e...

Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning

The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. U...