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Several methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.
This article was published in the following journal.
Name: BMJ open respiratory research
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The aim of this study was to develop and validate a decision support model using machine learning algorithm to predict treatment success after single-session SWL in ureteral stone patients.
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In this prospective study, the ability of a machine learning algorithm to predict sepsis and influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).
The investigators propose to develop and evaluate a hospital department-specific machine learning based clinical decision support (CDS) system for early sepsis prediction, focused on impro...
A sepsis early warning predictive algorithm, InSight, has been developed and validated on a large, diverse patient cohort. In this prospective study, the ability of InSight to predict seve...
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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 ...
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 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.
Usually refers to the use of mathematical models in the prediction of learning to perform tasks based on the theory of probability applied to responses; it may also refer to the frequency of occurrence of the responses observed in the particular study.
Systemic inflammatory response syndrome with a proven or suspected infectious etiology. When sepsis is associated with organ dysfunction distant from the site of infection, it is called severe sepsis. When sepsis is accompanied by HYPOTENSION despite adequate fluid infusion, it is called SEPTIC SHOCK.
Sepsis, septicaemia and blood poisoning
Septicaemia (another name for blood poisoning) refers to a bacterial infection of the blood, whereas sepsis can also be caused by viral or fungal infections. Sepsis is not just limited to the blood and can affect the whole body, including the organ...
Clinical Approvals Clinical Trials Drug Approvals Drug Delivery Drug Discovery Generics Drugs Prescription Drugs In the fields of medicine, biotechnology and pharmacology, drug discovery is the process by which drugs are dis...