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When Leonard D'Avolio (Figure 1) was working on his Ph.D. degree in biomedical informatics, he saw the power of machine learning in transforming multiple industries; health care, however, was not among them. "The reason that Amazon, Netflix, and Google have transformed their industries is because they have embedded learning throughout every aspect of what they do. If we could prove that is possible in health care too, I thought we would have the potential to have a huge impact," he says.
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Name: IEEE pulse
Further improvements in population health in low- and middle-income countries demand high-quality care to address an increasingly complex burden of disease. Health facility surveys provide an importan...
There have been tremendous advances in artificial intelligence and machine learning within the past decade, especially in the application of deep learning to various challenges. These include advanced...
Machine learning is a method for predicting clinically relevant variables, such as opportunities for early intervention, potential treatment response, prognosis, and health outcomes. This commentary e...
In their Perspective, Ara Darzi and Hutan Ashrafian give us a tour of the future policymaker's machine learning toolkit.
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The use of handheld arterial 'stethoscopes' (continuous wave Doppler devices) are ubiquitous in clinical practice. However, most users have received no formal training in their use or the ...
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...
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 proposed, mono-institutional, randomized-controlled trial aims to determine whether the dosimetric outcomes following prostate Low-Dose-Rate (LDR) brachytherapy, planned using a novel ...
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.
Nurses whose work combines elements of both primary care nursing and public health practice and takes place primarily outside the therapeutic institution. Primary nursing care is directed to individuals, families, or groups in their natural settings within communities.