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Relapse rates are consistently high for stimulant user disorders. In order to obtain prognostic information about individuals in treatment, machine learning models have been applied to neuroimaging an...
Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Mach...
Machine learning has exhibited powerful capabilities in many areas. However, machine learning models are mostly database dependent, requiring a new model if the database changes. Therefore, a universa...
Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence. They can approximate functions and dynamics by learning fro...
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML's contributions, both realized and potential, across several areas of syst...
Cholera is an acute enteric infectious disease caused by the bacterium Vibrio cholera, leading to watery diarrhea and loss of fluids from the small intestines. The World Health Organizatio...
Cholera is a severe diarrhea illness caused by bacteria. The purpose of this study is to better understand how the immune systems of people in Dhaka, Bangladesh, fight infection with chole...
A study is necessary in order to assess the safety and immunogenicity of the bivalent killed oral cholera vaccine produced in India by Shantha Biotechnics among healthy adult and children ...
The purpose of this study is to conduct cholera vaccinations in high-risk populations in Zanzibar in order to estimate herd protection conferred by the vaccine,estimate effectiveness of th...
The primary objective of this study is to evaluate the antibody response to the cholera vaccine, Vaxchora®, in healthy subjects. Investigators also seek to evaluate additional markers of...
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled 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)
Antiretroviral Therapy Clostridium Difficile Ebola HIV & AIDS Infectious Diseases Influenza Malaria Measles Sepsis Swine Flu Tropical Medicine Tuberculosis Infectious diseases are caused by pathogenic...