AIMC Topic: Machine Learning

Clear Filters Showing 23081 to 23090 of 34417 articles

Bayesian additive regression trees and the General BART model.

Statistics in medicine
Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mainstream, there is an increased need for a paper that walks readers throu...

A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning.

IEEE journal of biomedical and health informatics
Automatic sleep staging methods usually extract hand-crafted features or network trained features from signals recorded by polysomnography (PSG), and then estimate the stages by various classifiers. In this study, we propose a classification approach...

Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records.

Computer methods and programs in biomedicine
OBJECTIVE: Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step ...

Transformed ℓ regularization for learning sparse deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Deep Neural Networks (DNNs) have achieved extraordinary success in numerous areas. However, DNNs often carry a large number of weight parameters, leading to the challenge of heavy memory and computation costs. Overfitting is another challenge for DNN...

Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.

Medical physics
PURPOSE: Intensity-modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verify dose calculation and delivery accuracy. In this work, we applied a machine learning-based approach...

Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study.

The Lancet. Digital health
BACKGROUND: Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approac...

Development and Validation of the Automated Imaging Differentiation in Parkinsonism (AID-P): A Multi-Site Machine Learning Study.

The Lancet. Digital health
BACKGROUND: There is a critical need to develop valid, non-invasive biomarkers for Parkinsonian syndromes. The current 17-site, international study assesses whether non-invasive diffusion MRI (dMRI) can distinguish between Parkinsonian syndromes.

Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study.

Scientific reports
A comprehensive screening method using machine learning and many factors (biological characteristics, Helicobacter pylori infection status, endoscopic findings and blood test results), accumulated daily as data in hospitals, could improve the accurac...