Machine learning is an exciting technology with broad application in big data analysis, as well as increasingly in specialised healthcare. As a diagnostic tool in tissue workup and pathology, it has the potential for personalised and stratified appro...
BACKGROUND: Determining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the r...
With the advances in different biological networks including gene regulation, gene co-expression, protein-protein interaction networks, and advanced approaches for network reconstruction, analysis, and interpretation, it is possible to discover relia...
BACKGROUND: This study is to evaluate the accuracy of machine learning for differentiation between optic neuropathies, pseudopapilledema (PPE) and normals.
Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing ...
INTRODUCTION: Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learnin...
Forensic science international. Genetics
Aug 8, 2019
DNA mixture interpretation remains one of the major challenges in forensic DNA analysis. DNA mixture samples are inherently complex due to several factors including the variations in the quantity of DNA, the presence of non-allelic artifactual peaks ...
This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who u...
Computer methods and programs in biomedicine
Aug 8, 2019
BACKGROUND AND OBJECTIVE: A feed-forward neural network (FNN) is a type of artificial neural network that has been widely used in medical diagnosis, data mining, stock market analysis, and other fields. Many studies have used FNN to develop medical d...
In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled envi...
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