Critical Care

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 estimation is ill-conditioned and typically requires computationally expensive iterative forward simulation. This study investigates whether a supervised neural network (NN) can provide a fast inverse estimator for a paediatric sepsis cardiovascular ODE ...

TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond

Human mobility differs from text and from generic time series in three structural ways: visits are tuple-valued events whose meaning depends on the joint distribution over location, time, and activity; users carry persistent signatures across trajectories; and visits are not independent across users, since co-location at shared places is a primary signal. Existing pre-training recipes for mobility...

BioResearcher: Scenario-Guided Multi-Agent for Translational Medicine

Translational medicine turns underspecified development goals into evidence synthesis that must combine literature, trials, patents, and quantitative ...

MSD-Score: Multi-Scale Distributional Scoring for Reference-Free Image Caption Evaluation

Evaluating image captions without references remains challenging because global embedding similarity often misses fine-grained mismatches such as hall...

MARBLE: Multi-Aspect Reward Balance for Diffusion RL

Reinforcement learning fine-tuning has become the dominant approach for aligning diffusion models with human preferences. However, assessing images is...

Heterogeneous Treatment Effect for Targeted Temperature Management After Cardiac Arrest: A Causal Machine Learning Analysis

ABSTRACT Objectives: To determine whether heterogeneous treatment effects (HTE) explain the inconclusive results of targeted temperature management (T...

Multi-LLM Disagreement as a Scalable Detector of Human Annotation Errors in Structured Data from Clinical Free-Text

Abstract Objective: Structured extraction from clinical free-text depends on human annotators whose labels are susceptible to errors and knowledge-dri...

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...

Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Pools

We propose Conformal Seasonal Pools (CSP), a training-free probabilistic time-series forecaster that mixes same-season empirical draws with signed res...

A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability

In recent years, machine learning has made significant progress in clinical outcome prediction, demonstrating increasingly accurate results. However, ...

From Data Lifting to Continuous Risk Estimation: A Process-Aware Pipeline for Predictive Monitoring of Clinical Pathways

This paper presents a reproducible and process-aware pipeline for predictive monitoring of clinical pathways. The approach integrates data lifting, te...

A Digital Twin for Tracking and Forecasting Glycemia with Septic Patients in ICUs

We present a digital twin framework for real time glucose monitoring and forecasting in septic patients in intensive care units (ICUs). The framework ...

Stop Holding Your Breath: CT-Informed Gaussian Splatting for Dynamic Bronchoscopy

Bronchoscopic navigation relies on registering endoscopic video to a preoperative CT scan, but respiratory motion deforms the airway by 5-20 mm, creat...

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...

Are Data Augmentation and Segmentation Always Necessary? Insights from COVID-19 X-Rays and a Methodology Thereof

Purpose: Rapid and reliable diagnostic tools are crucial for managing respiratory diseases like COVID-19, where chest X-ray analysis coupled with arti...

VLM Judges Can Rank but Cannot Score: Task-Dependent Uncertainty in Multimodal Evaluation

Vision-language models (VLMs) are increasingly used as automated judges for multimodal systems, yet their scores provide no indication of reliability....

Detection of bronchopulmonary dysplasia in infants and prediction of school-age lung function from tidal breathing data using recurrent neural networks

Objective: To test whether machine learning (ML) models trained on tidal breathing flow time series can discriminate between individuals with and with...

VLM Judges Can Rank but Cannot Score: Task-Dependent Uncertainty in Multimodal Evaluation

Vision-language models (VLMs) are increasingly used as automated judges for multimodal systems, yet their scores provide no indication of reliability....

Exploring Remote Photoplethysmography for Neonatal Pain Detection from Facial Videos

Unaddressed pain in neonates can lead to adverse effects, including delayed development and slower weight gain, emphasising the need for more objectiv...