Intensivists

Improving machine learning and deep learning models for 30-day ICU readmission prediction using Ensemble Bayesian Model Averaging

Intensive Care Unit (ICU) readmissions are associated with adverse clinical outcomes and increased healthcare costs. Although existing models for predicting 30-day ICU readmission show high predictive performance, they fail to account for model uncertainty, potentially resulting in overconfident and unreliable decision-making. We propose a novel Ensemble Bayesian Model Averaging (EBMA)-based frame...

Multi-Narrow Transformation as a Single-Model Ensemble: Boundary Conditions, Mechanisms, and Failure Modes

Single-model ensembles (SMEs) have attracted attention as a way to approximate some of the benefits of deep ensembles within a single network. However, under an approximately matched parameter budget, it remains unclear whether model capacity should be concentrated in a single wide pathway or redistributed into many narrow and independent members. We investigate this question through the Multi-Nar...

Focusable Monocular Depth Estimation

Monocular depth foundation models generalize well across scenes, yet they are typically optimized with uniform pixel-wise objectives that do not disti...

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...

Neural Network Guided Calibration for Fast Virtual Twin Generation in Cardiovascular ODE Models

Calibration of closed-loop lumped-parameter cardiovascular models remains a major bottleneck for scalable digital-twin generation because inverse esti...

Calibration Drift Under Cross-Institutional Deployment: An External Validation Framework for ICU Mortality Prediction Across MIMIC-IV and eICU

Background: Machine learning models for intensive care unit (ICU) mortality prediction achieve strong internal discrimination yet rarely undergo exter...

Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks

While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memorizing sensitive personal infor...

Non-Invasive Arterial Blood Pressure Waveform Generation in Critically Ill Patients: A Sensor-Based Deep Learning Approach

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of t...

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 ...

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...

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...

Generalizing intensive care AI across time scales in resource-limited settings

Temporal resolution of physiological monitoring in intensive care varies widely across healthcare systems. Artificial intelligence models assume a uni...

GeneBench: Assessing AI Agents for Multi-Stage Inference Problems in Genomics and Quantitative Biology

We introduce GeneBench, a benchmark for AI agents on realistic multi-stage scientific data analysis in genetics and quantitative biology. Existing bio...

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...

Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment

Wastewater treatment plants (WWTPs) need digital-twin-style decision support tools that can simulate plant response under prescribed control plans, to...

A Study of Failure Modes in Two-Stage Human-Object Interaction Detection

Human-object interaction (HOI) detection aims to detect interactions between humans and objects in images. While recent advances have improved perform...

SemiFA: An Agentic Multi-Modal Framework for Autonomous Semiconductor Failure Analysis Report Generation

Semiconductor failure analysis (FA) requires engineers to examine inspection images, correlate equipment telemetry, consult historical defect records,...

ReflectCAP: Detailed Image Captioning with Reflective Memory

Detailed image captioning demands both factual grounding and fine-grained coverage, yet existing methods have struggled to achieve them simultaneously...

Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation

Although recent advances have improved the quality of 3D texture generation, existing methods still struggle with incomplete texture coverage, cross-v...