AIMC Topic: Machine Learning

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Model-Based Generation of Large Databases of Cardiac Images: Synthesis of Pathological Cine MR Sequences From Real Healthy Cases.

IEEE transactions on medical imaging
Collecting large databases of annotated medical images is crucial for the validation and testing of feature extraction, statistical analysis, and machine learning algorithms. Recent advances in cardiac electromechanical modeling and image synthesis p...

Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry.

Brain and behavior
INTRODUCTION: Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task m...

A deep learning approach for predicting the quality of online health expert question-answering services.

Journal of biomedical informatics
Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers ...

Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

Journal of applied physiology (Bethesda, Md. : 1985)
Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o) by w...

Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set-based face recognition, where each training and testing example contains a set of face images, which were captured from different variations of pose,...

Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomnography Resources?

Methods of information in medicine
OBJECTIVES: The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic va...

Predicting anatomic therapeutic chemical classification codes using tiered learning.

BMC bioinformatics
BACKGROUND: The low success rate and high cost of drug discovery requires the development of new paradigms to identify molecules of therapeutic value. The Anatomical Therapeutic Chemical (ATC) Code System is a World Health Organization (WHO) proposed...

Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants.

Scientific reports
Disease and trait-associated variants represent a tiny minority of all known genetic variation, and therefore there is necessarily an imbalance between the small set of available disease-associated and the much larger set of non-deleterious genomic v...

Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning.

Scientific reports
Vitreomacular adhesion (VMA) represents a prognostic biomarker in the management of exudative macular disease using anti-vascular endothelial growth factor (VEGF) agents. However, manual evaluation of VMA in 3D optical coherence tomography (OCT) is l...

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.

Biomedical engineering online
BACKGROUND: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well...