Hospital-Based Medicine

Intensivists

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

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ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning.

Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate tr...

Multi-file dynamic compression method based on classification algorithm in DNA storage.

The exponential growth in data volume has necessitated the adoption of alternative storage solutions...

Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Histopathology image-based survival prediction aims to provide a precise assessment of cancer progno...

Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models.

Ensuring the security of China's rice harvest is imperative for sustainable food production. The exi...

Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke.

Ischemic lesion segmentation and the time since stroke (TSS) onset classification from paired multi-...

Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.

Cephalometric analysis is critically important and common procedure prior to orthodontic treatment a...

CMR-net: A cross modality reconstruction network for multi-modality remote sensing classification.

In recent years, the classification and identification of surface materials on earth have emerged as...

Optimal use of β-lactams in neonates: machine learning-based clinical decision support system.

BACKGROUND: Accurate prediction of the optimal dose for β-lactam antibiotics in neonatal sepsis is c...

AutoAMS: Automated attention-based multi-modal graph learning architecture search.

Multi-modal attention mechanisms have been successfully used in multi-modal graph learning for vario...

Research on multi-defects classification detection method for solar cells based on deep learning.

Solar cells are playing a significant role in aerospace equipment. In view of the surface defect cha...

Triplet-aware graph neural networks for factorized multi-modal knowledge graph entity alignment.

Multi-Modal Entity Alignment (MMEA), aiming to discover matching entity pairs on two multi-modal kno...

Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit.

This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of ...

AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images.

Early detection of pneumoconiosis by routine health screening of workers in the mining industry is c...

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion.

The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare se...

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Findin...

Multi-label classification of retinal diseases based on fundus images using Resnet and Transformer.

Retinal disorders are a major cause of irreversible vision loss, which can be mitigated through accu...

Multi-output neural network model for predicting biochar yield and composition.

In biomass pyrolysis for biochar production, existing prediction models face computational challenge...

Sepsis mortality prediction with Machine Learning Tecniques.

OBJECTIVE: To develop a sepsis death classification model based on machine learning techniques for p...

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