Intensivists

MANDARIN: Mixture-of-Experts Framework for Dynamic Delirium and Coma Prediction in ICU Patients: Development and Validation of an Acute Brain Dysfunction Prediction Model

Acute brain dysfunction (ABD) is a common, severe ICU complication, presenting as delirium or coma and leading to prolonged stays, increased mortality, and cognitive decline. Traditional screening tools like the Glasgow Coma Scale (GCS), Confusion Assessment Method (CAM), and Richmond Agitation-Sedation Scale (RASS) rely on intermittent assessments, causing delays and inconsistencies. In this st...

GrInAdapt: Scaling Retinal Vessel Structural Map Segmentation Through Grounding, Integrating and Adapting Multi-device, Multi-site, and Multi-modal Fundus Domains

Retinal vessel segmentation is critical for diagnosing ocular conditions, yet current deep learning methods are limited by modality-specific challenges and significant distribution shifts across imaging devices, resolutions, and anatomical regions. In this paper, we propose GrInAdapt, a novel framework for source-free multi-target domain adaptation that leverages multi-view images to refine segm...

CMMCoT: Enhancing Complex Multi-Image Comprehension via Multi-Modal Chain-of-Thought and Memory Augmentation

While previous multimodal slow-thinking methods have demonstrated remarkable success in single-image understanding scenarios, their effectiveness be...

Screening of mitochondrial-related biomarkers connected with immune infiltration for acute respiratory distress syndrome through WGCNA and machine learning.

Septic acute respiratory distress syndrome (ARDS) is a complex and noteworthy type, but its molecular mechanism has not been fully elucidated. The aim...

Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge

Foundation models (FMs) such as GPT-4 exhibit exceptional generative capabilities across diverse downstream tasks through fine-tuning. Split Federat...

Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation

Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucia...

Multi-View Depth Consistent Image Generation Using Generative AI Models: Application on Architectural Design of University Buildings

In the early stages of architectural design, shoebox models are typically used as a simplified representation of building structures but require ext...

Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction.

The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a bett...

Federated transfer learning with differential privacy for multi-omics survival analysis.

Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of features is significantly larger than the sample size,...

Development of a Deep Learning Model for the Prediction of Ventilator Weaning

The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficul...

Bridging Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive Learning

Deploying depth estimation networks in the real world requires high-level robustness against various adverse conditions to ensure safe and reliable ...

Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images

Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical structures to perform disease diagnosis. An automati...

Machine Learning-Based Rapid Prediction of Torsional Performance of Personalized Peripheral Artery Stent.

The complex mechanical environment of peripheral arteries makes stents with poor torsional performance more prone to fracture, and stent fracture is c...

LMHLD: A Large-scale Multi-source High-resolution Landslide Dataset for Landslide Detection based on Deep Learning

Landslides are among the most common natural disasters globally, posing significant threats to human society. Deep learning (DL) has proven to be an...

Improving Representation Learning of Complex Critical Care Data with ICU-BERT

The multivariate, asynchronous nature of real-world clinical data, such as that generated in Intensive Care Units (ICUs), challenges traditional AI-...

Nonlinear Sparse Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data

Motivation: Biomedical studies increasingly produce multi-view high-dimensional datasets (e.g., multi-omics) that demand integrative analysis. Exist...

Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome

Background: The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is...

Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts

This paper introduces Multi-Modal Retrieval-Augmented Generation (M^2RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large L...

A novel approach to navigate the taxonomic hierarchy to address the Open-World Scenarios in Medicinal Plant Classification

In this article, we propose a novel approach for plant hierarchical taxonomy classification by posing the problem as an open class problem. It is ob...

A Novel Multi-Task Teacher-Student Architecture with Self-Supervised Pretraining for 48-Hour Vasoactive-Inotropic Trend Analysis in Sepsis Mortality Prediction

Sepsis is a major cause of ICU mortality, where early recognition and effective interventions are essential for improving patient outcomes. However,...