Critical Care

Outcome Prediction Models for Critically Ill Patients Using Small Routine Laboratory Datasets

We present a suite of foundational, outcome prediction models for critically ill patients, developed using readily available, routine blood tests and advanced machine learning techniques. The input data of the models includes complete blood counts (CBCs), metabolic panels, and additional biomarkers that assess liver and kidney function, coagulation status, and cardiac injury. The output yields the...

Artificial-Intelligence-Enabled Early Malnutrition Risk Assessment Tools for Elderly Trauma Patients in Intensive Care Units

Background & Aims: Accurate assessment of clinical malnutrition using anthropometric and functional indicators could improve the care of elderly trauma patients in intensive care units (ICUs). This study aimed to develop an AI-driven malnutrition assessment toolbox based on a minimal set of clinically feasible indicators. Methods: Multiple machine learning models, including logistic regression, su...

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...

Generative Augmentation Reveals Previously Overlooked Signals in Transcriptomic Datasets

Identifying robust gene expression signatures from transcriptomic studies with small sample sizes remains one of the most persistent challenges in com...

Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification

Training reliable respiratory sound classification models remains challenging due to the limited size and subject diversity of datasets. Ensemble meth...

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...

GlioVision: A Multi-Modal MRI Framework for Non-Invasive Glioma Molecular Biomarkers Prediction

Gliomas are aggressive primary brain tumors that necessitate critical molecular biomarker predictions for optimal clinical decision-making. Traditiona...

Integrative Clinical-Molecular Modeling Identifies LRRN4CL as a Determinant of Structural and Functional Myocardial Improvement

Background: Mechanical ventricular unloading and systemic circulatory support with left ventricular assist devices (LVADs) enable myocardial recovery ...

From Protocol to Practice: Graded Sepsis Bundle Compliance and Actionable Insights from Real-World ICU Data

Sepsis is a leading cause of in-hospital mortality, yet systematically evaluating temporal adherence to the Surviving Sepsis Campaign (SSC) bundle acr...

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 struc...

Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis

Respiratory airflow signals provide critical insight into breathing mechanics, yet conventional analysis methods remain limited in their ability to ch...

An autonomous LLM agent platform for computational binder design and conjugation-aware prioritization of antibody drug conjugates

Large language model (LLM) agents have automated tool use in chemistry, but orchestrating multi step computational biology workflows spanning structur...

Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness

Multimodal clinical records contain structured measurements and clinical notes recorded over time, offering rich temporal information about the evolut...

Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics

Timely and interpretable early warning of sepsis remains a major clinical challenge due to the complex temporal dynamics of physiological deterioratio...

Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning

Multi-spectral imagery is a valuable input signal for Remote Sensing applications, such as land-use and land-cover classification and environmental mo...

Opportunistic Bone-Loss Screening from Routine Knee Radiographs Using a Multi-Task Deep Learning Framework with Sensitivity-Constrained Threshold Optimization

Background: Osteoporosis and osteopenia are often undiagnosed until fragility fractures occur. Dual-energy X-ray absorptiometry (DXA) is the reference...

S2H-DPO: Hardness-Aware Preference Optimization for Vision-Language Models

Vision-Language Models (VLMs) have demonstrated remarkable progress in single-image understanding, yet effective reasoning across multiple images rema...

Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning

Reinforcement Fine-Tuning (RFT) has established itself as a critical paradigm for the alignment of Multi-modal Large Language Models (MLLMs) with comp...

Can LLMs Score Medical Diagnoses and Clinical Reasoning as well as Expert Panels?

Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adju...

CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction

Accurate prediction of future risk and disease progression in sepsis is clinically important for early warning and timely intervention in intensive ca...