Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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A multi-stage multi-modal learning algorithm with adaptive multimodal fusion for improving multi-label skin lesion classification.

Skin cancer is frequently occurring and has become a major contributor to both cancer incidence and ...

A skin disease classification model based on multi scale combined efficient channel attention module.

Skin diseases, a significant category in the medical field, have always been challenging to diagnose...

Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study.

BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have ex...

Multi-agent deep reinforcement learning-based robotic arm assembly research.

Due to the complexity and variability of application scenarios and the increasing demands for assemb...

Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.

OBJECTIVES: Developing and validating interpretable machine learning (ML) models for predicting whet...

MVGNCDA: Identifying Potential circRNA-Disease Associations Based on Multi-view Graph Convolutional Networks and Network Embeddings.

Increasing evidences have indicated that circular RNAs play a crucial role in the onset and progress...

Multi-label segmentation of carpal bones in MRI using expansion transfer learning.

The purpose of this study was to develop a robust deep learning approach trained with a smallMRI dat...

Prior knowledge-based multi-task learning network for pulmonary nodule classification.

The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for...

A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery.

BACKGROUND: Sepsis remains a life-threatening condition in intensive care units (ICU) with high morb...

Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.

While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluoro...

Constraining an Unconstrained Multi-agent Policy with offline data.

Real-world multi-agent decision-making systems often have to satisfy some constraints, such as harmf...

Multi-step depth enhancement refine network with multi-view stereo.

This paper introduces an innovative multi-view stereo matching network-the Multi-Step Depth Enhancem...

Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.

ELM (Extreme learning machine) has drawn great attention due its high training speed and outstanding...

A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data.

Gene microarray technology provides an efficient way to diagnose cancer. However, microarray gene ex...

Precise multi-factor immediate implant placement decision models based on machine learning.

This study aims to explore the effect of implant apex design, osteotomy preparation, intraosseous de...

Diffusion-driven multi-modality medical image fusion.

Multi-modality medical image fusion (MMIF) technology utilizes the complementarity of different moda...

Hierarchical task network-enhanced multi-agent reinforcement learning: Toward efficient cooperative strategies.

Navigating multi-agent reinforcement learning (MARL) environments with sparse rewards is notoriously...

EMBANet: A flexible efficient multi-branch attention network.

Recent advances in the design of convolutional neural networks have shown that performance can be en...

Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis.

The variability in image modalities presents significant challenges in medical image classification,...

Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition.

The detection and classification of arrhythmia play a vital role in the diagnosis and management of ...

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