Practice Management

Staffing & Scheduling

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

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High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.

Primary malignant bone tumors, such as osteosarcoma, significantly affect the pediatric and young ad...

Genetic Artificial Hummingbird Algorithm-Support Vector Machine for Timely Power Theft Detection.

Utilities face serious obstacles from power theft, which calls for creative ways to maintain income ...

Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis.

Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in hi...

A Generalized Attention Mechanism to Enhance the Accuracy Performance of Neural Networks.

In many modern machine learning (ML) models, attention mechanisms (AMs) play a crucial role in proce...

Fractional whale driving training-based optimization enabled transfer learning for detecting autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a neurological illness that degrades communication and interaction...

Developing Machine Vision in Tree-Fruit Applications-Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting.

Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Netw...

Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.

In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for spec...

Artificial intelligence-based motion tracking in cancer radiotherapy: A review.

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk...

Robot-assisted gait training in patients with various neurological diseases: A mixed methods feasibility study.

BACKGROUND: Walking impairment represents a relevant symptom in patients with neurological diseases ...

Machine learning revealing overlooked conjunction of working volume and mixing intensity in anammox optimization.

Extensive studies on improving anammox performance have taken place for decades with particular focu...

Performance enhancement of deep learning based solutions for pharyngeal airway space segmentation on MRI scans.

The automatic segmentation of the pharyngeal airway space has many potential medical use, one of whi...

Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis.

The current study aimed to systematically review the literature on the accuracy of artificial intell...

Inter-participant transfer learning with attention based domain adversarial training for P300 detection.

A Brain-computer interface (BCI) system establishes a novel communication channel between the human ...

Recruitment in Appalachian, Rural and Older Adult Populations in an Artificial Intelligence World: Study Using Human-Mediated Follow-Up.

BACKGROUND: Participant recruitment in rural and hard-to-reach (HTR) populations can present unique ...

Deep Learning Powers Protein Identification from Precursor MS Information.

Proteome analysis currently heavily relies on tandem mass spectrometry (MS/MS), which does not fully...

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.

In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution....

SNN-BERT: Training-efficient Spiking Neural Networks for energy-efficient BERT.

Spiking Neural Networks (SNNs) are naturally suited to process sequence tasks such as NLP with low p...

Explainable machine learning framework to predict the risk of work-related neck and shoulder musculoskeletal disorders among healthcare professionals.

OBJECTIVE: This study aims to develop risk prediction models for neck and shoulder musculoskeletal d...

Explainable machine learning-driven predictive performance and process parameter optimization for caproic acid production.

In this study, four machine learning (ML) prediction models were developed to predict and optimize t...

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