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10.1038/s41598-018-28611-7 http://scihub22266oqcxt.onion/10.1038/s41598-018-28611-7 C6035173!6035173!29980727     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2018 ; 8 (ä): ä Nephropedia Template TP {{tp|p=29980727|t=2018. Lupus nephritis pathology prediction with clinical indices |pdf=|usr=}}{{29980727}} gab.com Text Lupus nephritis pathology prediction with clinical indices JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29980727 #FOAvip Effective treatment of lupus nephritis and assessment of patient prognosis depend on accurate pathological classification and careful use of acute and chronic pathological indices. Renal biopsy can provide most reliable predicting power. However, clinicians still need auxiliary tools under certain circumstances. Comprehensive statistical analysis of clinical indices may be an effective support and supplementation for biopsy. In this study, 173 patients with lupus nephritis were classified based on histology and scored on acute and chronic indices. These results were compared against machine learning predictions involving multilinear regression and random forest analysis. For three class random forest analysis, total classification accuracy was 51.3% (class II 53.7%, class III&IV 56.2%, class V 40.1%). For two class random forest analysis, class II accuracy reached 56.2%; class III&IV 63.7%; class V 61%. Additionally, machine learning selected out corresponding important variables for each class prediction. Multiple linear regression predicted the index of chronic pathology (CI) (Q2?=?0.746, R2?=?0.771) and the acute index (AI) (Q2?=?0.516, R2?=?0.576), and each variable?s importance was calculated in AI and CI models. Evaluation of lupus nephritis by machine learning showed potential for assessment of lupus nephritis. Twit Text FOAVip Lupus nephritis pathology prediction with clinical indices ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29980727 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29980727 #FOAvip English Wikipedia <ref>{{Cite pmid|29980727}}</ref> Lupus nephritis pathology prediction with clinical indices #MMPMID29980727 Tang Y; Zhang W; Zhu M; Zheng L; Xie L; Yao Z; Zhang H; Cao D; Lu B Sci Rep 2018[]; 8 (ä): ä PMID29980727show ga Effective treatment of lupus nephritis and assessment of patient prognosis depend on accurate pathological classification and careful use of acute and chronic pathological indices. Renal biopsy can provide most reliable predicting power. However, clinicians still need auxiliary tools under certain circumstances. Comprehensive statistical analysis of clinical indices may be an effective support and supplementation for biopsy. In this study, 173 patients with lupus nephritis were classified based on histology and scored on acute and chronic indices. These results were compared against machine learning predictions involving multilinear regression and random forest analysis. For three class random forest analysis, total classification accuracy was 51.3% (class II 53.7%, class III&IV 56.2%, class V 40.1%). For two class random forest analysis, class II accuracy reached 56.2%; class III&IV 63.7%; class V 61%. Additionally, machine learning selected out corresponding important variables for each class prediction. Multiple linear regression predicted the index of chronic pathology (CI) (Q2?=?0.746, R2?=?0.771) and the acute index (AI) (Q2?=?0.516, R2?=?0.576), and each variable?s importance was calculated in AI and CI models. Evaluation of lupus nephritis by machine learning showed potential for assessment of lupus nephritis. äLupus nephritis pathology prediction with clinical indices (epmc).pdf DeepDyve Pubget Overpricing