Information Technology

TogoMCP: Natural Language Querying of Life-Science Knowledge Graphs via Schema-Guided LLMs and the Model Context Protocol

Querying the RDF Portal knowledge graph maintained by DBCLS, which aggregates approximately 60 life-science databases, requires proficiency in both SPARQL and database-specific RDF schemas, placing this resource beyond the reach of most researchers. Large Language Models (LLMs) can, in principle, translate natural-language questions into executable SPARQL, but without schema-level context, they fr...

DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System

Accurate disease trajectory prediction is critical for early intervention, resource allocation, and improving long-term outcomes. While electronic health records (EHRs) provide a rich longitudinal view of patient health in clinical environments, models trained on curated research cohorts may not reflect routine deployment settings, and those trained on single-hospital datasets capture only fragmen...

Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment

Reconstructing precise clinical timelines is essential for modeling patient trajectories and forecasting risk in complex, heterogeneous conditions lik...

Structured large language model extraction of clinical factors from electronic health record text supports scalable psychiatric severity prediction

Background: Mental health systems face escalating demand that exceeds clinician capacity, making accurate severity-based triage a critical bottleneck....

From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction

By processing electronic health records (EHRs) as natural language sequences, large language models (LLMs) have shown potential in clinical prediction...

EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records

Electronic Health Records (EHR) contain rich longitudinal patient information and are widely used in predictive modeling applications. However, effect...

Characterization of menopause onset and associated disease risks using large-scale electronic health records

Menopause affects over one billion women worldwide, yet remains poorly characterized at scale. We apply an ICD-10-based phenotyping algorithm to elect...

Cadence: A Benchmark Evaluation of the Narrative Velocity Framework for Next Clinical Event Prediction in MIMIC-IV

Objective: How structured clinical features and cluster-semantic embeddings interact under self-distillation in EHR prediction models is unknown. Exis...

Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories

We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories. JEPA architec...

Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE

Modern cybersecurity relies heavily on static machine-learning-based malware classifiers. However, transformations such as packing and other non-seman...

Early Detection of Rare Disease Using Hierarchical Set-to-Sequence Modeling of Structured Electronic Health Records

Rare diseases are characterized by heterogeneous, weak, and sparse phenotypic signals that emerge gradually across longitudinal clinical visits, makin...

Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T therapy using large language models.

Chimeric Antigen Receptor T-cell (CAR-T) therapy, where genetically engineered patient T cells target tumor antigens, has transformed care for hematol...

Enhance the after-discharge mortality rate prediction via learning from the medical notes

With the increase of the Electronic Health Records (EHR) data, more and more researchers are developing machine learning models to learn from the medi...

Deep Kernel Learning for Stratifying Glaucoma Trajectories

Effectively stratifying patient risk in chronic diseases like glaucoma is a major clinical challenge. Clinicians need tools to identify patients at hi...

Disease Risk Prediction Using Structured EHR Data: Can Generalist Large Language Models Match Specialized Clinical Foundation Models? A Comparative Evaluation with Fine-Tuning

Background: Electronic health records (EHRs) with clinical decision support tools are now ubiquitous in healthcare organizations. Clinical foundation ...

Differentiable latent structure discovery for interpretable forecasting in clinical time series

Background: Timely, uncertainty-aware forecasting from irregular electronic health records (EHR) can support critical-care decisions, yet most approac...

Detecting Clinical Discrepancies in Health Coaching Agents: A Dual-Stream Memory and Reconciliation Architecture

As Large Language Model (LLM) agents transition from single-session tools to persistent systems managing longitudinal healthcare journeys, their memor...

Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study

Background Timely detection is crucial to improve outcomes in patients with cardiac amyloidosis (CA) by initiation of life-saving treatments. Although...

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...

Dialysis Risk Prediction and Treatment Effect Estimation for AKI patients using Longitudinal Electronic Health Records

Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures in...