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A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables.

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Title: A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables.
Authors: Downing MJ; Department of Pathology, Brigham and Women's Hospital (M.J.D., D.J.P., G.L.M.) Department of Pathology, Harvard Medical School (D.J.P., G.L.M.) Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute (S.T.) Department of Biostatistics, Harvard T. H. Chan School of Public Health (S.T.), Boston, Massachusetts., Papke DJ Jr, Tyekucheva S, Mutter GL
Source: International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists [Int J Gynecol Pathol] 2020 Jul; Vol. 39 (4), pp. 333-343.
Publication Type: Journal Article
Journal Info: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 8214845 Publication Model: Print Cited Medium: Internet ISSN: 1538-7151 (Electronic) Linking ISSN: 02771691 NLM ISO Abbreviation: Int J Gynecol Pathol Subsets: MEDLINE
Database: MEDLINE Ultimate
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        Value: 10.1097/PGP.0000000000000615
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        Text: English
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        StartPage: 333
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      – TitleFull: A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables.
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            NameFull: Tyekucheva S
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            NameFull: Mutter GL
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            – D: 01
              M: 07
              Text: 2020 Jul
              Type: published
              Y: 2020
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            – TitleFull: International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists
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