Hospital-Based Medicine

Intensivists

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

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A Novel and Powerful Dual-Stream Multi-Level Graph Convolution Network for Emotion Recognition.

Emotion recognition enables machines to more acutely perceive and understand users' emotional states...

Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields.

This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy s...

Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm.

This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients i...

Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning.

Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) o...

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction.

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. C...

Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images.

Breast cancer (BC) is a type of cancer which progresses and spreads from breast tissues and graduall...

Enhancing urban flow prediction via mutual reinforcement with multi-scale regional information.

Intelligent Transportation Systems (ITS) are essential for modern urban development, with urban flow...

Long-term water quality assessment in coastal and inland waters: An ensemble machine-learning approach using satellite data.

Accurate estimation of coastal and in-land water quality parameters is important for managing water ...

MuSE: A deep learning model based on multi-feature fusion for super-enhancer prediction.

Although bioinformatics-based methods accurately identify SEs (Super-enhancers), the results depend ...

Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

OBJECTIVE: The objective was to establish a machine learning-based model for predicting the occurren...

AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging.

The widespread prevalence of sleep problems in children highlights the importance of timely and accu...

Critical care studies using large language models based on electronic healthcare records: A technical note.

The integration of large language models (LLMs) in clinical medicine, particularly in critical care,...

UMS-ODNet: Unified-scale domain adaptation mechanism driven object detection network with multi-scale attention.

Unsupervised domain adaptation techniques improve the generalization capability and performance of d...

Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.

Positron emission tomography (PET) utilizes radiotracers like [F]fluorodeoxyglucose (FDG) to measure...

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution-Pooling Method.

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power...

A novel benign and malignant classification model for lung nodules based on multi-scale interleaved fusion integrated network.

One of the precursors of lung cancer is the presence of lung nodules, and accurate identification of...

Explainable machine learning for early prediction of sepsis in traumatic brain injury: A discovery and validation study.

BACKGROUND: People with traumatic brain injury (TBI) are at high risk for infection and sepsis. The ...

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