Critical Care

Predicting Healthcare System Visitation Flow by Integrating Hospital Attributes and Population Socioeconomics with Human Mobility Data

Healthcare visitation patterns are influenced by a complex interplay of hospital attributes, population socioeconomics, and spatial factors. However, existing research often adopts a fragmented approach, examining these determinants in isolation. This study addresses this gap by integrating hospital capacities, occupancy rates, reputation, and popularity with population SES and spatial mobility pa...

Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment

Sepsis remains one of the leading causes of mortality in intensive care units, where timely and accurate treatment decisions can significantly impact patient outcomes. In this work, we propose an interpretable decision support framework. Our system integrates four core components: (1) a clustering-based stratification module that categorizes patients into low, intermediate, and high-risk groups up...

GEOGRAPHIC DOMAIN SHIFT PRECIPITATES DIVERGENT FAILURE MODES IN DEEP LEARNING BASED TUBERCULOSIS SCREENING: A MULTI-NATIONAL EXTERNAL VALIDATION STUDY

Background: Deep learning algorithms for tuberculosis (TB) screening frequently achieve radiologist-level performance during internal evaluation, yet ...

Automated Assessment of OSCE Physical Exams using Multimodal AI

Background The assessment of physical examination skills in medical education is resource-intensive and prone to inter-rater variability. While artifi...

Inverse Rendering for High-Genus 3D Surface Meshes from Multi-view Images with Persistent Homology Priors

Reconstructing 3D objects from images is inherently an ill-posed problem due to ambiguities in geometry, appearance, and topology. This paper introduc...

Leveraging Explainable Temporal-Modelling Machine Learning to Identify Distinct Multimorbidity Trajectory Profiles in Acute Myocardial Infarction

IntroductionAcute myocardial infarction (AMI) remains a leading cause of mortality, with the coexistence of other conditions (i.e., multimorbidity) co...

LeMoF: Level-guided Multimodal Fusion for Heterogeneous Clinical Data

Multimodal clinical prediction is widely used to integrate heterogeneous data such as Electronic Health Records (EHR) and biosignals. However, existin...

EvoMorph: Counterfactual Explanations for Continuous Time-Series Extrinsic Regression Applied to Photoplethysmography

Wearable devices enable continuous, population-scale monitoring of physiological signals, such as photoplethysmography (PPG), creating new opportuniti...

Achieving Expert-Level Clinical Infection Detection with LLMs from Clinical Documents: Validation in Complex Patient Cases with Cirrhosis

BackgroundSystemic infections are a leading cause of hospitalization and death among patients with cirrhosis. Timely and accurate infection identifica...

Integrative Multi-Omics Analysis Reveals Novel Molecular Signatures, Disease Stratification and Therapeutic Opportunities in Primary Ciliary Dyskinesia: First AI-ML empowered platform towards precision medicine targeting human ciliopathies

Primary ciliary dyskinesia (PCD) belongs to the group of rare genetic disorders that is extremely hard to diagnose and treat. Current diagnostic modal...

IMSAHLO: Integrating Multi-Scale Attention and Hybrid Loss Optimization Framework for Robust Neuronal Brain Cell Segmentation

Accurate segmentation of neuronal cells in fluorescence microscopy is a fundamental task for quantitative analysis in computational neuroscience. Howe...

Prediction of peripheral blood lymphocyte subpopulations after renal transplantation.

Immune monitoring is essential for maintaining immune homeostasis after renal transplantation (RT). Peripheral blood lymphocyte subpopulations (PBLSs)...

Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models

Prompt learning facilitates the efficient adaptation of Vision-Language Models (VLMs) to various downstream tasks. However, it faces two significant...

An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis

The labor-intensive nature of medical data annotation presents a significant challenge for respiratory disease diagnosis, resulting in a scarcity of...

ViLU: Learning Vision-Language Uncertainties for Failure Prediction

Reliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new V...

ViLU: Learning Vision-Language Uncertainties for Failure Prediction

Reliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new V...

Stable-Hair v2: Real-World Hair Transfer via Multiple-View Diffusion Model

While diffusion-based methods have shown impressive capabilities in capturing diverse and complex hairstyles, their ability to generate consistent a...

A novel approach for classifying Monoamine Neurotransmitters by applying Machine Learning on UV plasmonic-engineered Auto Fluorescence Time Decay Series (AFTDS)

This study introduces a hybrid approach integrating advanced plasmonic nanomaterials and machine learning (ML) for high-precision biomolecule detect...

MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction

Legal judgment prediction offers a compelling method to aid legal practitioners and researchers. However, the research question remains relatively u...

SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN

The growing demand for efficient knowledge graph (KG) enrichment leveraging external corpora has intensified interest in relation extraction (RE), p...