Hospital-Based Medicine

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

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Chinese medical dialogue information extraction via contrastive multi-utterance inference.

Medical Dialogue Information Extraction (MDIE) is a promising task for modern medical care systems, ...

Hyperspectral image super-resolution based on the transfer of both spectra and multi-level features.

Existing hyperspectral image (HSI) super-resolution methods fusing a high-resolution RGB image (HR-R...

DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction.

Most proteins in nature contain multiple folding units (or domains). The revolutionary success of Al...

Multi-Expert Deep Networks for Multi-Disease Detection in Retinal Fundus Images.

Automatic diagnosis of eye diseases from retinal fundus images is quite challenging. Common public d...

MSGAN: Multi-Stage Generative Adversarial Networks for Cross-Modality Domain Adaptation.

Domain adaptation has become an important topic because the trained neural networks from the source ...

MVD-Net: Semantic Segmentation of Cataract Surgery Using Multi-View Learning.

Semantic segmentation of surgery scenarios is a fundamental task for computer-aided surgery systems....

A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms.

Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer ba...

Treatment Prediction in the ICU Setting Using a Partitioned, Sequential Deep Time Series Analysis.

We developed a neural network architecture to evaluate the patient's state using temporal data, pati...

[Intelligent fault diagnosis expert system for multi-parameter monitor based on fault tree].

Aiming at the dilemma of expensive and difficult maintenance, lack of technical data and insufficien...

Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records.

OBJECTIVE: We aimed to develop a data-driven machine learning model for predicting critical deterior...

AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks.

Omics-based biomedical learning frequently relies on data of high-dimensions (up to thousands) and l...

Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images.

MOTIVATION: The development of microscopic imaging techniques enables us to study protein subcellula...

Multi-level spatial details cross-extraction and injection network for hyperspectral pansharpening.

Hyperspectral (HS) pansharpening, which fuses the HS image with a high spatial resolution panchromat...

Image-free multi-character recognition.

The recently developed image-free sensing technique decouples semantic information directly from com...

Multi-variable AUC for sifting complementary features and its biomedical application.

Although sifting functional genes has been discussed for years, traditional selection methods tend t...

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.

MOTIVATION: Effective computational methods to predict drug-protein interactions (DPIs) are vital fo...

Enhancing sepsis management through machine learning techniques: A review.

Sepsis is a major public health problem and a leading cause of death in the world, where delay in th...

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.

More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide po...

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning.

Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interac...

A roadmap for multi-omics data integration using deep learning.

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-...

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