Surveys

On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks.

Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due ...

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-sit...

A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders.

Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term...

For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth.

RATIONALE, AIMS AND OBJECTIVES: Artificial intelligence and big data are more and more used in medic...

Decoding Optical Data with Machine Learning.

Optical spectroscopy and imaging techniques play important roles in many fields such as disease diag...

Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data.

Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the...

Data-driven dose calculation algorithm based on deep U-Net.

Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiat...

Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology - national survey.

BACKGROUND: Artificial Intelligence (AI) is making a continuous progression into the field of Radiat...

The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies.

Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but ...

Comparing the Ability of Regression Modeling and Bayesian Additive Regression Trees to Predict Costs in a Responsive Survey Design Context.

Responsive survey designs rely upon incoming data from the field data collection to optimize cost an...

Data-Driven Robust Control for a Closed-Loop Artificial Pancreas.

We present a fully closed-loop design for an artificial pancreas (AP) that regulates the delivery of...

Data classification based on fractional order gradient descent with momentum for RBF neural network.

The weight-updating methods have played an important role in improving the performance of neural net...

Parents' views on artificial intelligence for the daily management of childhood asthma: a survey.

Machine Learning Techniques for Ophthalmic Data Processing: A Review.

Machine learning and especially deep learning techniques are dominating medical image and data analy...

Deep Learning for Time Series Forecasting: A Survey.

Time series forecasting has become a very intensive field of research, which is even increasing in r...

The influence of machine learning-based knowledge management model on enterprise organizational capability innovation and industrial development.

The aims are to explore the construction of the knowledge management model for engineering cost cons...

Global adoption of robotic technology into neurosurgical practice and research.

Recent technological advancements have led to the development and implementation of robotic surgery ...

Big data clustering techniques based on Spark: a literature review.

A popular unsupervised learning method, known as clustering, is extensively used in data mining, mac...

Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach.

This paper aims to further increase the reliability of optimal results by setting the simulation con...

Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey.

BACKGROUND: Artificial intelligence (AI) has the potential to disrupt how we diagnose and treat pati...

Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.

Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obt...