Practice Management

Staffing & Scheduling

Latest AI and machine learning research in staffing & scheduling for healthcare professionals.

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Showing 2668-2688 of 6,187 articles
Effects of robotic gait training after stroke: A meta-analysis.

BACKGROUND: Robotic devices are often used in rehabilitation and might be efficient to improve walki...

Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.

High-quality annotations for medical images are always costly and scarce. Many applications of deep ...

Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy.

A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology w...

Multi-way backpropagation for training compact deep neural networks.

Depth is one of the key factors behind the success of convolutional neural networks (CNNs). Since Re...

New optimization algorithms for neural network training using operator splitting techniques.

In the following paper we present a new type of optimization algorithms adapted for neural network t...

Robotic Cholecystectomy Is a Safe Educational Alternative to Laparoscopic Cholecystectomy During General Surgical Training: A Pilot Study.

OBJECTIVE: The role of robotic surgery in general surgery (GS) continues to expand. Several programs...

Effects of Robot-Assisted Gait Training in Individuals with Spinal Cord Injury: A Meta-analysis.

BACKGROUND: To investigate the effects of robot-assisted gait training (RAGT) on spasticity and pain...

Impact of hybrid supervision approaches on the performance of artificial intelligence for the classification of chest radiographs.

PURPOSE: To evaluate the impact of different supervision regimens on the training of artificial inte...

Matching patients to clinical trials using semantically enriched document representation.

Recruiting eligible patients for clinical trials is crucial for reliably answering specific question...

Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces.

Recent technological advances have enabled researchers to collect large amounts of electroencephalog...

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligib...

One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis.

Determining the number of factors is one of the most crucial decisions a researcher has to face when...

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb.

The retention of a capsule endoscope (CE) in the stomach and the duodenal bulb during the examinatio...

Training a Convolutional Neural Network with Terminology Summarization Data Improves SNOMED CT Enrichment.

As a step toward learning to automatically insert new concepts into a large biomedical ontology, we ...

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

In recent years, protein-ligand interaction scoring functions derived through machine-learning are r...

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.

Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuab...

Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review.

BACKGROUND: Application of machine learning for classifying human behavior is increasingly common as...

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