Information Technology

In silico perturbations provide multivariate interpretability in predicting post-lung transplant outcomes

Lung transplantation is a life-saving therapy for end-stage lung disease but has the poorest survival among solid organ transplants. We analyzed standardized electronic health record (EHR) data from the United Network for Organ Sharing (UNOS) to predict one-, three-, and five-year survival and favorable long-term outcomes post-lung transplant. We applied two multivariate machine learning approache...

A Bilingual On-premise AI agent for Clinical Drafting: Seamless EHR integration in the Y-KNOT Project

Large Language Models (LLMs) have shown promise in reducing clinical documentation burden, yet their real-world implementation faces significant challenges, particularly in non-English speaking countries with strict data sovereignty requirements. Here we present Your-Knowledgeable Navigator of Treatment (Y-KNOT), the first successful implementation of an on-premise bilingual LLM-based artificial i...

The gSOS Polygenic Score is Associated with Bone Density and Fracture Risk in Childhood

The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using machine learning algorithms in adults of European a...

Towards Inpatient Discharge Summary Automation via Large Language Models: A Multidimensional Evaluation with a HIPAA-Compliant Instance of GPT-4o and Clinical Expert Assessment

Large language models (LLMs) have demonstrated potential to automate clinical documentation tasks that may reduce clinician burden, such as generation...

AutoRADP: An Interpretable Deep Learning Framework to Predict Rapid Progression for Alzheimer’s Disease and Related Dementias Using Electronic Health Records

Alzheimer’s disease (AD) and AD-related dementias (ADRD) exhibit heterogeneous progression rates, with rapid progression (RP) posing significant chall...

Current Limitations of Electronic Health Record Systems in Supporting Pragmatic Clinical Trials: Insights from the eMERGE Consortium

Pragmatic clinical trials (PCTs) evaluate interventions in real-world settings, often using electronic health records (EHRs) for efficient data collec...

Scalable Identification of Clinically Relevant COPD Documents: A Lightweight NLP Model for Large-Scale EHR Datasets

The widespread adoption of electronic health records (EHRs) has resulted in the generation of large volumes of clinical notes. Learning algorithms and...

Integrating a host transcriptomic biomarker with a large language model for diagnosis of lower respiratory tract infection

Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide and can be difficult to diagnose in critically ill patients, as ...

Machine Learning-Based Mortality Prediction in Critically Ill Patients with Hypertension: Comparative Analysis, Fairness, and Interpretability

Hypertension is a leading global health concern, significantly contributing to cardiovascular, cerebrovascular, and renal diseases. In critically ill ...

TrialGenie: Empowering Clinical Trial Design with Agentic Intelligence and Real World Data

Clinical trial design (CTD) is a time-consuming process that requires substantial domain expertise. Large-scale real-world data (RWD), such as electro...

Longitudinal Masked Representation Learning for Pulmonary Nodule Diagnosis from Language Embedded EHRs

Electronic health records (EHRs) are a rich source of clinical data, yet exploiting longitudinal signals for pulmonary nodule diagnosis remains challe...

Evaluation of Machine Learning Models for Early Prediction of Gestational Diabetes Using Retrospective Electronic Health Records from Current and Previous Pregnancies

To assess the performance of machine learning (ML) models in predicting gestational diabetes mellitus (GDM) using electronic health record (EHR) data ...

Changes in psychiatric documentation and treatment in primary care with artificial intelligence scribe use

Despite increasingly widespread use of artificial intelligence-driven ambient scribes in medicine, the extent to which they may impact clinician pract...

Benchmarking transformer-based models for medical record deidentification: A single centre, multi-specialty evaluation

Robust de-identification is necessary to preserve patient confidentiality and maintain public acceptance of electronic health record (EHR) research. M...

RT-HaND-C: A Multi-Source, Validated Real-World Head and Neck Cancer Dataset for Research

Real-world data (RWD) is essential in head and neck cancer (HNC) research, offering insights into outcomes among diverse, comorbid patients often unde...

Comparing Machine and Deep Learning Models for Pediatric Anxiety Classification using Structured EHRs and Area-based Measures of Health Data

This study investigates the performance of various machine learning (ML) and deep learning (DL) models to classify pediatric patients at risk of anxie...

DeepDrug2: A Germline-focused Graph Neural Network Framework for Alzheimer’s Drug Repurposing Validated by Electronic Health Records

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with limited therapeutic options. The original DeepDrug framework by Li et al. (2025)...

Assessment of the Modified Rankin Scale in Electronic Health Records with a Fine-tuned Large Language Model

The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observational studie...

ClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in Medicine

Integrating structured clinical knowledge into artificial intelligence (AI) models remains a major challenge. Medical codes primarily reflect administ...

Development of the Short Hospitalization Predictor (SHoP) Machine Learning Model Across Two Hospitals

To develop and evaluate an open-source machine learning (ML) models for predicting hospital short stays (length of stay [LOS] under 48 and 72 hours) e...