An end stage kidney disease predictor based on an artificial neural networks ensemble

被引：41

作者：

Di Noia, Tommaso ^{[1
]}

Ostuni, Vito Claudio ^{[1
]}

Pesce, Francesco ^{[2
,3
]}

Binetti, Giulio ^{[1
,4
]}

Naso, David ^{[1
]}

Schena, Francesco Paolo ^{[2
]}

Di Sciascio, Eugenio ^{[1
]}

机构：

[1] Politecn Bari, Dept Elect & Elect Engn, Bari, Italy

[2] Univ Bari, Nephrol Dialysis & Transplantat Unit, Dept Emergency & Organ Transplantat, Bari, Italy

[3] Univ London Imperial Coll Sci Technol & Med, Natl Heart & Lung Inst, London SW7 2AZ, England

[4] Univ Texas Arlington, Automat & Robot Res Inst, Arlington, TX 76019 USA

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2013年 / 40卷 / 11期

关键词：

Clinical decision support system (CDSS); Neural networks ensemble; End stage kidney disease; Machine learning; IGA NEPHROPATHY; STRATIFICATION; SURVIVAL; RISK;

D O I：

10.1016/j.eswa.2013.01.046

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

IgA Nephropathy (IgAN) is a worldwide disease that affects kidneys in human beings and leads to end-stage kidney disease (ESKD) thus requiring renal replacement therapy with dialysis or kidney transplantation. The need for new tools able to help clinicians in predicting ESKD risk for IgAN patients is highly recognized in the medical field. In this paper we present a software tool that exploits the power of artificial neural networks to classify patients' health status potentially leading to ESKD. The classifier lever-ages the results returned by an ensemble of 10 networks trained by using data collected in a period of 38 years at University of Bari. The developed tool has been made available both as an online Web application and as an Android mobile app. Noteworthy to its clinical usefulness is that its development is based on the largest available cohort worldwide. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：4438 / 4445

页数：8

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