Practice Management

Staffing & Scheduling

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

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Showing 2479-2499 of 6,187 articles
An Electrically Actuated Soft Artificial Muscle Based on a High-Performance Flexible Electrothermal Film and Liquid-Crystal Elastomer.

Liquid-crystal elastomer (LCE)-based soft robots and devices via an electrothermal effect under a lo...

Pitfalls in training and validation of deep learning systems.

The number of publications in endoscopic journals that present deep learning applications has risen ...

Iterative Training of Neural Networks for Intra Prediction.

This paper presents an iterative training of neural networks for intra prediction in a block-based i...

Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training.

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promisi...

The future surgical training paradigm: Virtual reality and machine learning in surgical education.

Surgical training has undergone substantial change in the last few decades. As technology and patien...

Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search.

Automating the classification of camera-obtained microscopic images of White Blood Cells (WBCs) and ...

Pre-training phenotyping classifiers.

Recent transformer-based pre-trained language models have become a de facto standard for many text c...

Statistical stopping criteria for automated screening in systematic reviews.

Active learning for systematic review screening promises to reduce the human effort required to iden...

Generating photo-realistic training data to improve face recognition accuracy.

Face recognition has become a widely adopted biometric in forensics, security and law enforcement th...

Training confounder-free deep learning models for medical applications.

The presence of confounding effects (or biases) is one of the most critical challenges in using deep...

Digital Gaming Interventions in Psychiatry: Evidence, Applications and Challenges.

Human evolution has regularly intersected with technology. Digitalization of various services has br...

Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control.

This paper introduces a new generative deep learning network for human motion synthesis and control....

Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging.

PURPOSE: Post-reconstruction filtering is often applied for noise suppression due to limited data co...

Locomotor and robotic assistive gait training for children with cerebral palsy.

AIM: To determine if robotic assisted gait training (RAGT) using surface muscle electrical stimulati...

Detecting cells in intravital video microscopy using a deep convolutional neural network.

The analysis of leukocyte recruitment in intravital video microscopy (IVM) is essential to the under...

Combined virtual reality and haptic robotics induce space and movement invariant sensorimotor adaptation.

Prism adaptation is a method for studying visuomotor plasticity in healthy individuals, as well as f...

Evaluation of the feasibility of an error-minimized approach to powered wheelchair skills training using shared control.

BACKGROUND: Powered wheelchairs promote participation for people with mobility limitations. For olde...

A Deep Learning Pipeline for Nucleus Segmentation.

Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in bi...

Using digital technologies in clinical trials: Current and future applications.

In 2015, we provided an overview of the use of digital technologies in clinical trials, both as a me...

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