The Clinician and Dataset Shift in Artificial Intelligence

This letter outlines how to identify, and potentially mitigate, common sources of “dataset shift” in machine-learning systems. This occurs when the model “training data” differ from the data used by the model to provide diagnostic, prognostic, or treatment advice.

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Podrobná bibliografie
Vydáno v:	The New England journal of medicine Ročník 385; číslo 3; s. 283 - 286
Hlavní autoři:	Finlayson, Samuel G, Subbaswamy, Adarsh, Singh, Karandeep, Bowers, John, Kupke, Annabel, Zittrain, Jonathan, Kohane, Isaac S, Saria, Suchi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Massachusetts Medical Society 15.07.2021
Témata:	Accuracy Algorithms and Education Artificial Intelligence Calibration Clinical medicine Clinical Practice Clinical trials COVID-19 Datasets Datasets as Topic Demographics Health IT Health Policy Hospitalist Hospitals Humans Influenza Information Technology Learning algorithms Machine Learning Medical Informatics Medical Practice Patients Primary Care Quality of Care Reimbursement Sepsis Software Training Trends
ISSN:	0028-4793, 1533-4406, 1533-4406
On-line přístup:	Získat plný text
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