Hospital-Based Medicine

Intensivists

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

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Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy.

Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in a...

SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis.

The integration of morphological attributes extracted from histopathological images and genomic data...

Early prognosis prediction for non-variceal upper gastrointestinal bleeding in the intensive care unit: based on interpretable machine learning.

INTRODUCTION: This study aims to construct a mortality prediction model for patients with non-varice...

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning.

Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view informa...

MAPRS: An intelligent approach for post-prescription review based on multi-label learning.

Antimicrobial resistance (AMR) is a major threat to public health worldwide. It is a promising way t...

Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.

BACKGROUND: Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criti...

Multimodal fusion network for ICU patient outcome prediction.

Over the past decades, massive Electronic Health Records (EHRs) have been accumulated in Intensive C...

A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception.

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception an...

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data.

The generation of synthetic patient data that reflect the statistical properties of real data plays ...

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in t...

Multi-view scene matching with relation aware feature perception.

For scene matching, the extraction of metric features is a challenging task in the face of multi-sou...

Evaluation of tumor budding with virtual panCK stains generated by novel multi-model CNN framework.

As the global incidence of cancer continues to rise rapidly, the need for swift and precise diagnose...

Attention-based stackable graph convolutional network for multi-view learning.

In multi-view learning, graph-based methods like Graph Convolutional Network (GCN) are extensively r...

DiagSWin: A multi-scale vision transformer with diagonal-shaped windows for object detection and segmentation.

Recently, Vision Transformer and its variants have demonstrated remarkable performance on various co...

Multi-task heterogeneous graph learning on electronic health records.

Learning electronic health records (EHRs) has received emerging attention because of its capability ...

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation.

Deep learning plays a pivotal role in retinal blood vessel segmentation for medical diagnosis. Despi...

Augmenting patient monitoring during intravenous moderate sedation with artificial intelligence: A pilot study.

PURPOSE/OBJECTIVES: A precordial stethoscope (PS) is essential for ensuring clear breath sounds duri...

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