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This article was published in the following journal.
Name: The Journal of the American Academy of Orthopaedic Surgeons
Multidetector row computed tomography (CT) allows noninvasive imaging of the heart and coronary arteries. The purpose of this review is to briefly summarize recent advances in CT hardware and software...
Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impa...
Relevance and penetration of machine learning in clinical practice is a recent phenomenon with multiple applications being currently under development. Deep learning-and especially convolutional neura...
Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as...
Systems metabolic engineering allows efficient development of high performing microbial strains for the sustainable production of chemicals and materials. In recent years, increasing availability of b...
This study will collect retrospective CT scan images and clinical data from patients with incidental lung nodules seen in hospitals across London. We will research whether we can use machi...
The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascu...
Gliomas are one of the most challenging tumors to treat, because areas of the apparently normal brain contain microscopic deposits of glioma cells; indeed, these occult cells are known to ...
Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this ris...
The present project will develop an automated machine learning approach using multi-modality data (imaging, laboratory, electrocardiography and questionnaire) to increase the understanding...
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.
Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)