Reimbursement

TEDDY: A Pediatric Foundation Model for Risk Forewarning from ICD-Coded Diagnostic Histories

Pediatric electronic health records capture developmentally structured clinical trajectories, yet their potential for generative healthcare foundation models remains largely unexplored. Here we present TEDDY (Temporal Event Decoder for Disease in Youth), a 1.84-million-parameter decoder transformer trained on approximately 73 million ICD-10 diagnoses from 1.6 million children at a single pediatric...

Data-driven Prediction of Fifteen-Year All-Cause Mortality among 2.3 Million Individuals in the VA

We present a data-driven framework to predict 15-year all-cause mortality using outpatient administrative records for 2.3 million Veterans in the largest integrated U.S. healthcare system. Rather than relying on predefined clinical phenotypes, we used the 1,000 most common outpatient medical codes from each of three data types/modalities: ICD-9 (Dx), Current Procedural Terminology (CPT), and presc...

Emergency Department Presenting Concerns Among Admissions With Hypercapnia: A Retrospective NLP Study of MIMIC-IV

Background Hypercapnia may indicate a primary ventilatory syndrome, a complication of another illness, or an epiphenomenon of severe disease. The pres...

Automated Multisource Electronic Frailty Index in Acute Ischemic Stroke: Development and Clinical Utility

Background: Frailty is common in acute ischemic stroke (AIS) and predicts poor outcomes, but is not routinely captured in acute stroke care. Manual fr...

OHCA-EXTRACT: Evaluating the Accuracy of a Large Language Model Pipeline for Out-of-Hospital Cardiac Arrest Case Identification and Utstein Variable Extraction

Background: Manual identification and abstraction of out-of-hospital cardiac arrest (OHCA) cases and Utstein template variables from electronic health...

ICD-10 Code Ambiguity Obscures Treatment-Eligible Adults with Spinal Muscular Atrophy: A Single-Center Chart Review and Patient Outreach Study

Background. Three disease-modifying therapies (DMTs) for spinal muscular atrophy (SMA) have been approved since 2016, yet many adults remain untreated...

How Post-Training Shapes Biological Reasoning Models

Scientific reasoning models for biology combine language models with foundation models trained on multimodal biological data, including DNA, RNA, and ...

Nomenclature Ontology for Medical And Disease names (NOMAD): taxonomy of types and origins of disease names

The nomenclature of human disease has developed organically over the past centuries using Greek, Latin, and Arabic terminology and reflects the idiosy...

OmniGene-4: A Unified Bio-Language MoE Model with Router-Level Interpretability

How do multi-modal large language models that jointly process natural language and biological sequences (DNA, protein, structural alphabets) actually ...

Augmenting Structured Diagnoses through Effective Use of Pre-trained Large Language Models on Clinical Notes

Objective Clinical narrative provides a unique window into provider reasoning and attribution, but use has been limited by resource requirements and e...

Rethinking Low-Light Image Enhancement: A Log-Domain Intensity--Chromaticity Decoupling Perspective

Explicit reconstruction constraints derived from the decoupled representation are further imposed to suppress abnormal channel amplification and chrom...

Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation

The scarcity of high-quality annotated medical data, particularly in mental health, poses a significant bottleneck for training robust machine learnin...

Multi-Hospital Electronic Health Record Foundation Models Without Data Sharing: A Comparison of Federated Learning and Inference-Time Ensembling

Background. Foundation models for electronic health records (EHRs) perform strongly on clinical prediction, but every published model has been trained...

MedSafe-Dx (v0): A Safety-Focused Benchmark for Evaluating LLMs in Clinical Diagnostic Decision Support

MedSafe-Dx (v0), introduces a new safety-focused benchmark for evaluating large language models in clinical diagnostic decision support using a filter...

MEDICALBENCH: EVALUATING LARGE LANGUAGE MODELS TOWARDS IMPROVED MEDICAL CONCEPT EXTRACTION

Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful...

Beyond Independent Frames: Latent Attention Masked Autoencoders for Multi-View Echocardiography

Echocardiography is a widely used modality for cardiac assessment due to its non-invasive and cost-effective nature, but the sparse and heterogeneous ...

Improving Risk Stratification in Hypertrophic Cardiomyopathy: A Novel Score Combining Echocardiography, Clinical, and Medication Data

Hypertrophic cardiomyopathy (HCM) requires accurate risk stratification to inform decisions regarding ICD therapy and follow-up management. Current es...

Knowledge-Guided Retrieval-Augmented Generation for Zero-Shot Psychiatric Data: Privacy Preserving Synthetic Data Generation

AI systems in healthcare research have shown potential to increase patient throughput and assist clinicians, yet progress is constrained by limited ac...

Monte Carlo Committee Simulation with Large Language Models for Predicting Drug Reimbursement Recommendations and Conditions: A Novel Neurosymbolic AI Approach

Background: Health technology assessment (HTA) agencies issue reimbursement recommendations that determine patient access to new therapies. Predicting...

Tracing Copied Pixels and Regularizing Patch Affinity in Copy Detection

Image Copy Detection (ICD) aims to identify manipulated content between image pairs through robust feature representation learning. While self-supervi...