Critical Care

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...

Fuzzy-Based Expert Supervision System for Feedback Controlled Oxygenation.

Supervision of mechanical ventilation is currently still performed by clinical staff. With the incre...

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...

Multi-Dimensional Laboratory Test Score as a Proxy for Health.

The standard of care for a physician to review laboratory tests results is to weigh each individual ...

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...

DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.

MOTIVATION: Drug discovery has witnessed intensive exploration of predictive modeling of drug-target...

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...

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis.

BACKGROUND: Current strategies for risk stratification and prediction of neonatal early-onset sepsis...

Rapid SARS-CoV-2 Inactivation in a Simulated Hospital Room Using a Mobile and Autonomous Robot Emitting Ultraviolet-C Light.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since 2019 has made mask-...

Invited Commentary: "Robotic Airway Resection Under Spontaneous Ventilation".

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-...

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug inte...