Practice Management

Staffing & Scheduling

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

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Domain-Adaptive Fall Detection Using Deep Adversarial Training.

Fall detection (FD) systems are important assistive technologies for healthcare that can detect emer...

CMOS-Compatible Protonic Programmable Resistor Based on Phosphosilicate Glass Electrolyte for Analog Deep Learning.

Ion intercalation based programmable resistors have emerged as a potential next-generation technolog...

Transfer-RLS method and transfer-FORCE learning for simple and fast training of reservoir computing models.

Reservoir computing is a machine learning framework derived from a special type of recurrent neural ...

The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions.

In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to f...

Robot-mediated overground gait training for transfemoral amputees with a powered bilateral hip orthosis: a pilot study.

BACKGROUND: Transfemoral amputation is a serious intervention that alters the locomotion pattern, le...

Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms.

The forecast of electricity demand has been a recurrent research topic for decades, due to its econo...

Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records.

IMPORTANCE: Physicians are required to work with rapidly growing amounts of medical data. Approximat...

SAR ATR for Limited Training Data Using DS-AE Network.

Although automatic target recognition (ATR) with synthetic aperture radar (SAR) images has been one ...

Design of a Robotic Coach for Motor, Social and Cognitive Skills Training Toward Applications With ASD Children.

Socially assistive robots may help the treatment of autism spectrum disorder(ASD), through games usi...

Emotion Recognition on Edge Devices: Training and Deployment.

Emotion recognition, among other natural language processing tasks, has greatly benefited from the u...

How will artificial intelligence change medical training?

Artificial intelligence is changing medicine and it will relieve physicians from the burden of rote ...

Optimization and Simulation of Enterprise Management Resource Scheduling Based on the Radial Basis Function (RBF) Neural Network.

In the human resource system of modern enterprises, human-post matching big data occupies an importa...

Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning.

Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequent...

Self-paced and self-consistent co-training for semi-supervised image segmentation.

Deep co-training has recently been proposed as an effective approach for image segmentation when ann...

Artificial intelligence in tumor subregion analysis based on medical imaging: A review.

Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence...

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs.

IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of...

A deep learning approach to student registered nurse anesthetist (SRNA) education.

OBJECTIVES: This manuscript describes the application of deep learning to physiology education of St...

Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process.

The purpose of this study was to evaluate the diagnostic performance achieved by using fully-connect...

Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example.

The occupational profiling system driven by the traditional survey method has some shortcomings such...

Autoencoder based self-supervised test-time adaptation for medical image analysis.

Deep neural networks have been successfully applied to medical image analysis tasks like segmentatio...

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