Information Technology

FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records

Feature engineering for Electronic Health Records (EHR) is complicated by irregular observation intervals, variable measurement frequencies, and structural sparsity inherent to clinical time series. Existing automated methods either lack clinical domain awareness or assume clean, regularly sampled inputs, limiting their applicability to real-world EHR data. We present \textbf{FeatEHR-LLM}, a frame...

A Systematic Exploration of LLM Behavior for EHR phenotyping

Background Electronic health record (EHR) phenotyping underpins observational research, cohort discovery, and clinical trial screening. Large language models (LLMs) offer new capabilities for extracting phenotypes from unstructured text, but their performance depends on pipeline design choices-including prompting, text segmentation, and aggregation. No systematic framework has previously examined ...

Semaglutide is associated with improved breast cancer survival, lower metastatic burden, and a dose-survival relationship uncoupled from weight-loss magnitude

Metabolic dysfunction is increasingly recognized as a risk factor for poor outcomes in breast cancer, but whether incretin-based therapies confer surv...

MIMIC-IV-Phenotype-Atlas (MIPA) : A Publicly Available Dataset for EHR Phenotyping

Introduction Secondary use of electronic health records (EHRs) often requires transforming raw clinical information into research-grade data. A centra...

Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images

Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging b...

Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records

This study developed a large language model (LLM)-based solution to identify people at HIV risk using electronic health records. We transformed struct...

Validating a Deep Learning Algorithm to Identify Patients with Glaucoma using Systemic Electronic Health Records

We evaluated whether a glaucoma risk assessment (GRA) model trained on All of Us national data can identify patients at high probability of glaucoma u...

Evaluating Large Language Models for Transparent Quality-of-Care Measurement in Children with ADHD

ImportanceGuideline-concordant care for young children with attention-deficit/hyperactivity disorder (ADHD) includes recommending parent training in b...

M3D-Net: Multi-Modal 3D Facial Feature Reconstruction Network for Deepfake Detection

With the rapid advancement of deep learning in image generation, facial forgery techniques have achieved unprecedented realism, posing serious threats...

Community needs for FAIR pathogen data

Background: Datasets related to infectious diseases are essential for public health decision-making, yet their reuse remains limited by persistent bar...

ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection

Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging d...

Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation

In electronic health record (EHR) mining, learning high-quality representations of medical concepts (e.g., standardized diagnosis, medication, and pro...

Greater lean-body-mass decline with tirzepatide than semaglutide in routine care, revealed by body-composition digital phenotyping

GLP-1 receptor agonists induce substantial weight loss, but the extent to which lean tissue and physical function are preserved in routine care remain...

Nationwide Prediction of Missed and Cancelled Appointments Using Real-World EHR Data

ObjectivesTo develop and evaluate predictive models for unused outpatient appointments (missed or cancelled) using a large national electronic health ...

LLM-Driven Target Trial Emulation with Human-in-the-Loop Validation for Randomized Trial: Automated Protocol Extraction and Real-World Outcome Evaluation{Psi}

Target trial emulation (TTE) enables causal inference from observational data but remains bottlenecked by manual, expert-dependent protocol operationa...

TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection

Accurate estimation of cancer risk from longitudinal electronic health records (EHRs) could support earlier detection and improved care, but modeling ...

Identification and Anonymization of Named Entities in Unstructured Information Sources for Use in Social Engineering Detection

This study addresses the challenge of creating datasets for cybercrime analysis while complying with the requirements of regulations such as the Gener...

GraphWalker: Graph-Guided In-Context Learning for Clinical Reasoning on Electronic Health Records

Clinical Reasoning on Electronic Health Records (EHRs) is a fundamental yet challenging task in modern healthcare. While in-context learning (ICL) off...

Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering

In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinic...

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification

Kidney stone disease ranks among the most prevalent conditions in urology, and understanding the composition of these stones is essential for creating...