Predicting outcomes of chronic kidney disease from EMR data based on random forest regression

DOI10.1016/J.MBS.2019.02.001zbMath1425.92121OpenAlexW2912206724WikidataQ91622926 ScholiaQ91622926MaRDI QIDQ2328370

Jing Zhao, Adam McDermaid, Shaopeng Gu

Publication date: 10 October 2019

Published in: Mathematical Biosciences (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6435377

zbMATH Keywords

chronic kidney diseaseelectronic medical recordsrandom forest regression

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Related Items (1)

Editorial. The application of machine learning to disease diagnosis and treatment

Uses Software

• Scikit
• DeepPPI

Cites Work

• Unnamed Item

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