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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Masashi Goto1, Takashi Kawamura, Kenji Wakai
1Kyoto University Health Service, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan. goto@msa.biglobe.ne.jp
Predicting kidney disease progression in Immunoglobulin A nephropathy (IgAN) is challenging. A new risk stratification scheme using six clinical predictors accurately estimates IgAN patient outcomes.
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