Critical Care

Real-Time-Capable Muscle Force Estimation for Monitoring Robotic Rehabilitation Therapy in the Intensive Care Unit.

In this paper, a method is proposed to enable real-time monitoring of muscle forces during robotic r...

MosaicNet: A deep-learning-based multi-tile biomedical image stitching method.

Multi-tile image stitching aims to merge multiple natural or biomedical images into a single mosaic....

[A method for photoplethysmography signal quality assessment fusing multi-class features with multi-scale series information].

Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment...

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework.

OBJECTIVE: To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis pr...

[Research on multi-class orthodontic image recognition system based on deep learning network model].

To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep le...

Are we ready to adapt artificial intelligence for early sepsis detection?

Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.

MOTIVATION: With the great number of peptide sequences produced in the postgenomic era, it is highly...

An interpretable machine learning model for real-time sepsis prediction based on basic physiological indicators.

OBJECTIVE: In view of the important role of risk prediction models in the clinical diagnosis and tre...

Artificial Intelligence Solution for Chest Radiographs in Respiratory Outpatient Clinics: Multicenter Prospective Randomized Clinical Trial.

Artificial intelligence (AI)-assisted diagnosis imparts high accuracy to chest radiography (CXR) in...

Artificial Intelligence for Early Sepsis Detection: A Word of Caution.

The surgical anatomy of a (robot-assisted) minimally invasive transcervical esophagectomy.

BACKGROUND: Transcervical esophagectomy allows for esophagectomy through transcervical access and by...

Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.

Emerging studies have shown that circular RNAs (circRNAs) are involved in a variety of biological pr...

Attention-guided multi-scale deep object detection framework for lymphocyte analysis in IHC histological images.

Tumor-infiltrating lymphocytes are specialized lymphocytes that can detect and kill cancerous cells....

AFTGAN: prediction of multi-type PPI based on attention free transformer and graph attention network.

MOTIVATION: Protein-protein interaction (PPI) networks and transcriptional regulatory networks are c...

DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms.

Understanding the mechanisms of candidate drugs play an important role in drug discovery. The activa...

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning.

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis...

Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data.

Biomedical multi-modality data (also named multi-omics data) refer to data that span different types...

Continuous Renal Replacement Therapy: What Have We Learned And What Are Key Milestones For The Years To Come?

Continuous renal replacement therapy (CRRT) is the main extracorporeal kidney support therapy used i...

Developments in respiratory self-management interventions over the last two decades.

This paper describes developments in the fields of asthma and COPD self-management interventions (SM...

The possible role of artificial intelligence in deciding postnatal steroid management in extremely premature ventilated infants.

Clinical decision support (CDS) has shown a positive effect on physicians. There is variability amon...

A Self-Interpretable Deep Learning Model for Seizure Prediction Using a Multi-Scale Prototypical Part Network.

The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of seizures, wh...