Practice Management

Staffing & Scheduling

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

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Robot-Mediated Imitation Skill Training for Children With Autism.

Autism spectrum disorder (ASD) impacts 1 in 68 children in the U.S., with tremendous individual and ...

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review.

A comprehensive review on the problem of choosing a suitable activation function for the hidden laye...

A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility.

In this paper, we study a set of real-time scheduling problems whose objectives can be expressed as ...

Current state of virtual reality simulation in robotic surgery training: a review.

BACKGROUND: Worldwide, the annual number of robotic surgical procedures continues to increase. Robot...

How to build up the actionable knowledge base: the role of 'best fit' framework synthesis for studies of improvement in healthcare.

Increasing recognition of the role and value of theory in improvement work in healthcare offers the ...

A decentralized training algorithm for Echo State Networks in distributed big data applications.

The current big data deluge requires innovative solutions for performing efficient inference on larg...

OCReP: An Optimally Conditioned Regularization for pseudoinversion based neural training.

In this paper we consider the training of single hidden layer neural networks by pseudoinversion, wh...

An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts.

Pattern recognition in control charts is critical to make a balance between discovering faults as ea...

A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.

Automated phenotype identification plays a critical role in cohort selection and bioinformatics data...

Towards dropout training for convolutional neural networks.

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, ...

Performance comparison of multi-label learning algorithms on clinical data for chronic diseases.

We are motivated by the issue of classifying diseases of chronically ill patients to assist physicia...

Robot-supported upper limb training in a virtual learning environment : a pilot randomized controlled trial in persons with MS.

BACKGROUND: Despite the functional impact of upper limb dysfunction in multiple sclerosis (MS), effe...

Sample Selection for Training Cascade Detectors.

Automatic detection systems usually require large and representative training datasets in order to o...

An experimental comparison of the relative benefits of work and torque assistance in ankle exoskeletons.

Techniques proposed for assisting locomotion with exoskeletons have often included a combination of ...

Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs.

Support Vector Machines (SVMs) form a family of popular classifier algorithms originally developed t...

Weighting training images by maximizing distribution similarity for supervised segmentation across scanners.

Many automatic segmentation methods are based on supervised machine learning. Such methods have prov...

Characterization of running with compliant curved legs.

Running with compliant curved legs involves the progression of the center of pressure, the changes o...

Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression.

The Biological Magnetic Resonance Data Bank (BMRB) contains NMR chemical shift depositions for over ...

Proximal tibia fracture in a patient with incomplete spinal cord injury associated with robotic treadmill training.

STUDY DESIGN: One case report of proximal tibia fracture in a patient with incomplete spinal cord in...

A new robust model of one-class classification by interval-valued training data using the triangular kernel.

A robust one-class classification model as an extension of Campbell and Bennett's (C-B) novelty dete...

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