Analysis and Classification of Mammography Reports Using Maximum Variation Sampling

Analysis and Classification of Mammography Reports Using Maximum Variation Sampling [electronic resource]

Currently, no automated means of detecting abnormal mammograms exist. While knowledge discovery capabilities through data mining and data analytics tools are widespread in many industries, the healthcare industry as a whole lags far behind. Providers are only just beginning to recognize the value of...

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Bibliographic Details
Online Access: Online Access
Corporate Author: Oak Ridge National Laboratory (Researcher)
Format: Government Document Electronic eBook
Language:English
Published: Oak Ridge, Tenn. : Oak Ridge, Tenn. : Oak Ridge National Laboratory. ; distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2011.
Subjects:
Learning.
Algorithms.
Patients.
Validation.
Genetics.
Training.
Sensitivity.
Educational Facilities.
Mammary Glands.
Risk Assessment.
Testing.
Sampling.
Optimization.
Classification.
Diagnosis.
Mining.
Radiology.
Neoplasms.
Specificity.
Applied Life Sciences.
Basic Biological Sciences.

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Call Number: E 1.99:1009488
E 1.99:1009488 Available

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