Public Health & Policy

Work Force

Latest AI and machine learning research in work force for healthcare professionals.

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Evaluation of green lean production in textile industry: a hybrid fuzzy decision-making framework.

Textile industry is an old and effective industry in Iran. However, due to its age and high energy c...

Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force Fields.

Force-field development has undergone a revolution in the past decade with the proliferation of quan...

The impact of training sample size on deep learning-based organ auto-segmentation for head-and-neck patients.

To investigate the impact of training sample size on the performance of deep learning-based organ au...

A Few-Shot Learning-Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data.

Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, m...

Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques.

Biomedical natural language processing (NLP) has an important role in extracting consequential infor...

Assessing outcomes of ear molding therapy by health care providers and convolutional neural network.

Ear molding therapy is a nonsurgical technique to correct certain congenital auricular deformities. ...

Gait training with a wearable curara® robot for cerebellar ataxia: a single-arm study.

BACKGROUND: Ataxic gait is one of the most common and disabling symptoms in people with degenerative...

Neural network surgery: Combining training with topology optimization.

With ever increasing computational capacities, neural networks become more and more proficient at so...

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.

Finding optimal parameters for force fields used in molecular simulation is a challenging and time-c...

A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data.

BACKGROUND AND OBJECTIVE: Synthetic magnetic resonance imaging (MRI) is a low cost procedure that se...

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions.

Gait analysis based on inertial sensors has become an effective method of quantifying movement mecha...

Robot-Assisted Gait Training Plan for Patients in Poststroke Recovery Period: A Single Blind Randomized Controlled Trial.

BACKGROUND: Walking dysfunction exists in most patients after stroke. Evidence regarding gait traini...

Davis Computational Spectroscopy Workflow-From Structure to Spectra.

We describe an automated workflow that connects a series of atomic simulation tools to investigate t...

Implementation of a standardized robotic assistant surgical training curriculum.

Since 2000, robotic-assisted surgery has rapidly expanded into almost every surgical sub-specialty. ...

Developing a Robotic General Surgery Training Curriculum: Identifying Key Elements Through a Delphi Process.

OBJECTIVE: A national robotic surgery curriculum is still developing for general surgery residents a...

Semi-HIC: A novel semi-supervised deep learning method for histopathological image classification.

Histopathological images provide a gold standard for cancer recognition and diagnosis. Existing appr...

Informed training set design enables efficient machine learning-assisted directed protein evolution.

Directed evolution of proteins often involves a greedy optimization in which the mutation in the hig...

CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice.

More humans have died of tuberculosis (TB) than any other infectious disease and millions still die ...

Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results.

This study aims to determine how randomly splitting a dataset into training and test sets affects th...

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