Public Health & Policy

Work Force

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

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A new robot-based proprioceptive training algorithm to induce sensorimotor enhancement in the human wrist.

Afferent proprioceptive signals, responsible for body awareness, have a crucial role when planning a...

Predictive Analysis of Errors During Robot-Mediated Gamified Training.

This paper presents our approach to predicting future error-related events in a robot-mediated gamif...

Improving Ankle Muscle Recruitment via Plantar Pressure Biofeedback during Robot Resisted Gait Training in Cerebral Palsy.

Neurological impairment from stroke or cerebral palsy often presents with diminished ankle plantar f...

Redistributing Ground Reaction Forces During Squatting Using a Cable-Driven Robotic Device.

Squatting is a dynamic task that is often done for strengthening and improving balance. Most squat t...

Rethinking ImageNet Pre-training for Computational Histopathology.

Transfer learning from ImageNet pretrained weights is widely used when training Deep Learning models...

Classification of Chronic Venous Disorders using an Ensemble Optimization of Convolutional Neural Networks.

Chronic Venous Disorders (CVD) of lower limbs are one of the most prevalent medical conditions, affe...

Addressing the Intra-class Mode Collapse Problem using Adaptive Input Image Normalization in GAN-based X-ray Images.

Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adver...

Sequential Learning on sEMGs in Short- and Long-term Situations via Self-training Semi-supervised Support Vector Machine.

The purpose of this study it to assess the effect of sequential learning of self-training support ve...

Supervised and semi-supervised training of deep convolutional neural networks for gastric landmark detection.

This work focuses on detection of upper gas-trointestinal (GI) landmarks, which are important anatom...

Privacy-preserving Model Training for Disease Prediction Using Federated Learning with Differential Privacy.

Machine learning is playing an increasingly critical role in health science with its capability of i...

Beware the Black-Box of Medical Image Generation: an Uncertainty Analysis by the Learned Feature Space.

Deep neural networks (DNNs) are the primary driving force for the current development of medical ima...

[Research advances on functional training robots in burn rehabilitation].

Patients with deep burns are prone to suffer cicatrix hyperplasia or contracture, leading to problem...

Educating the Healthcare Workforce to Support Digital Transformation.

Digital transformation of the healthcare workforce is a priority if we are to leverage the potential...

Accuracies of Training Labels and Machine Learning Models: Experiments on Delirium and Simulated Data.

Supervised predictive models require labeled data for training purposes. Complete and accurate label...

Artificial Intelligence and Machine Learning Technologies in Cancer Care: Addressing Disparities, Bias, and Data Diversity.

Artificial intelligence (AI) and machine learning (ML) technologies have not only tremendous potenti...

Atom typing using graph representation learning: How do models learn chemistry?

Atom typing is the first step for simulating molecules using a force field. Automatic atom typing fo...

Artificial Intelligence Competencies in Postgraduate Medical Training in Germany.

Routine medical care is to be transformed by the introduction of artificial intelligence (AI), requi...

The Digital Transformation of Mental Health Care and Psychotherapy - A Market and Research Maturity Analysis.

Digital technology trends for mental health, instantiated with only emerging use cases or already es...

Burnout and Depression Detection Using Affective Word List Ratings.

Burnout syndrome and depression are prevalent mental health problems in many societies today. Most e...

Training Deep Convolutional Spiking Neural Networks With Spike Probabilistic Global Pooling.

Recent work on spiking neural networks (SNNs) has focused on achieving deep architectures. They comm...

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