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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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
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Popis
Shrnutí: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.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Correspondence-1
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ObjectType-Article-2
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Dr. Finlayson and Mr. Subbaswamy contributed equally to this letter.
ISSN:0028-4793
1533-4406
1533-4406
DOI:10.1056/NEJMc2104626