北京生物醫(yī)學(xué)工程

基于主成分分析的深度前饋神經(jīng)網(wǎng)絡(luò)的腎小球濾過率估算算法

Glomerular filtration rate estimation algorithm based on principal component analysis with deep feedforward neural network

作者：王露露楊震黃山張罡李飛詹曙

單位：大數(shù)據(jù)知識工程教育部重點實驗室（合肥 230601）<br />合肥工業(yè)大學(xué)計算機與信息學(xué)院（合肥 230601）<br />安徽醫(yī)科大學(xué)第二附屬醫(yī)院（合肥 230601）<br />通信作者：李飛。E-mail：[email protected]

關(guān)鍵詞：慢性腎臟病；腎小球濾過率；主成分分析；深度前饋神經(jīng)網(wǎng)絡(luò);估算模型

分類號：R318

出版年·卷·期（頁碼）：2023·42·2（164-169）

摘要：

目的提出一種基于主成分分析（principal component analysis，PCA）的深度前饋神經(jīng)網(wǎng)絡(luò)（deep feedforward neural network, DFNN），建立一個適用于中國慢性腎臟病人群的腎小球濾過率估算模型，并探討其在慢性腎臟病患者腎小球濾過率（glomerular filtration rate, GFR）估算中的應(yīng)用。方法受試者為2019年5月—2021年1月就診于安徽醫(yī)科大學(xué)第二附屬醫(yī)院、年齡<18歲的腎功能不穩(wěn)定，且排除服用甲氧芐啶或西咪替丁或接受透析的163例患者。本研究以腎動態(tài)顯像測定GFR為標準，建立主成分分析的深度前饋神經(jīng)網(wǎng)絡(luò)模型，以此估算GFR，同時將估算GFR結(jié)果與傳統(tǒng)CG方程和BP神經(jīng)網(wǎng)絡(luò)估算結(jié)果進行對比分析。結(jié)果通過PCA-DFNN-1神經(jīng)網(wǎng)絡(luò)訓(xùn)練出來的估算模型的15%符合率、30%符合率、50%符合率分別為為38.77%、55.1%、75.5%；曲線下面積ROC為0.845；Youden指數(shù)為0.58。結(jié)論提出的基于主成分分析的深度前饋神經(jīng)網(wǎng)絡(luò)模型有優(yōu)于CG方程和BP神經(jīng)網(wǎng)絡(luò)模型的結(jié)果，可以用于估算GFR。

Objective A deep feedforward neural network based on principal component analysis was proposed to establish a glomerular filtration rate estimation model suitable for Chinese chronic kidney disease population, and to explore its use in the estimation of glomerular filtration rate (GFR) in chronic kidney disease patients. Methods The participants were 163 patients who were visited the The Second Hospital of Anhui Medical University from May 2019 to January 2021, and were under 18 years old with unstable renal function, and were excluded from taking trimethoprim or crimetidine or receiving dialysis. In this study, the GFR was determined by dynamic renal imaging as the standard, and a deep feedforward neural network model of principal component analysis was established to estimate GFR. Results The 15%, 30%, and 50% coincidence rates of the estimated models trained by the PCA-DFNN-1 neural network were 38.77%, 55.1%, and 75.5%; the area under the curve ROC was 0.845; the Youden index was 0.58. Conclusions The proposed deep feedforward neural network model based on principal component analysis has better results than CG equation and BP neural network model, and can be used to estimate GFR.

參考文獻：

[1] 李麗琴. 基于人工智能的慢性腎病分級預(yù)警模型[J]. 時代報告（下半月）, 2012(5): 330-331.
[2] 徐靜. 徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)評估慢性腎臟病腎小球濾過率[D]. 大連: 大連醫(yī)科大學(xué), 2016.
Xu J. Evaluate glomerular filtration rate by the radial basis function neural network in patients with chronic kidney disease [D]. Dalian: Dalian Medical University, 2016.
[3] Zahran A, EI-Husseini A, Shoker A. Can cystatin C replace creatinine to estimate glomerular filtration rate? A literature review[J]. American Journal of Nephrology, 2007, 27(2): 197-205.
[4] Filler G, B?Kenkamp A, Hofmann W, et al. Cystatin C as a marker of GFR--history, indications, and future research[J]. Clinical Biochemistry, 2005, 38(1): 1-8.
[5] 陳有維, 萬福俊, 何樂愚, 等. ?BP 神經(jīng) 網(wǎng) 絡(luò) 用于評估腎小球濾過率的研究[J]. 臨床醫(yī)學(xué), 2008, 28(5): 107-109, 128.?
Chen YW, Wan FJ, He LY, et al. Study of BP neural network in predicting renal glomerular filtration rate[J].Clinical Medicine, 2008, 28(5): 107-109, 128.
[6] 張雨濃，劉迅，何良宇，等. 應(yīng)用WASD神經(jīng)網(wǎng)絡(luò)估算腎小球濾過率的研究[J]. 中國科技信息，2014(8): 212-216.
Zhang YN, Liu X, He LY, et al. Research on application of WASD neural network to estimating glomerular filtration rate[J]. China Science and Technology Information,2014(8): 212-216.?
[7] 張雨濃，何良宇，劉迅，等. 應(yīng)用RBF激勵WASD神經(jīng)網(wǎng)絡(luò)估算GFR[J]. 計算技術(shù)與自動化，2016, 35(1): 22-26.
Zhang YN, He LY, Liu X, et al. Application of RBF-activated ?WASD neuronet in estimating GFR[J]. Computing ? Technology and Automation, 2016, 35(1): 22-26.
[8] 鄒海英，李智，楊帆. 基于特征選擇的自適應(yīng)模糊神經(jīng)網(wǎng)絡(luò)在腎小球濾過率中的應(yīng)用[J]. 軟件導(dǎo)刊，2018, 17(6): 153-156.
[9] 李寧山，劉迅，吳效明，等. 人工神經(jīng)網(wǎng)絡(luò)在腎小球濾過率估算中的應(yīng)用[J]. 第三軍醫(yī)大學(xué)學(xué)報，2012, 34(5): 409-411.
Li NS, Liu X, Wu XM, et al. Estimating glomerular ?filtration ?rate with artificial neural network: a model establishment[J]. Acta Academiae Medicinae Militaris Tertiae, 2012, 34(5): 409-411.
[10] Li NS, Huang H, Qian HZ, et al. Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation[J]. ?Journal of Translational Medicine, 2020, 18: 120.
[11] Liu X, Pei XH, Li NS, et al. Improved glomerular filtration rate estimation by an artificial neural network[J]. PLoS One, 2013, 8(3): e58242.
[12] Liu X, Chen YR, Li NS, et al. Estimation of glomerular filtration rate by a radial basis function neural network in patients with type-2 diabetes mellitus[J]. BMC Neuphrology, 2013, 14: 181.
[13] Xu J, Guo BY, Liu CY. Evaluation of glomerular filtration rate in chronic kidney disease by radial basis function neural network[J]. Transplantation Proceedings, 2020, 52(3) : 748-753.
[14] 梁哲.改進的RBF神經(jīng)網(wǎng)絡(luò)在腎小球濾過率估算中的應(yīng)用[D]. 大連: 大連理工大學(xué), 2016.
Liang Z. Application of improved RBF neural networks on glomerular filtration rate estimation[D]. Dalian: Dalian University of Technology, 2016.
[15] 楊萬元，王勝男，趙衛(wèi)紅，等. 腎小球濾過率估算模型研究[J].生物醫(yī)學(xué)工程學(xué)雜志, 2013, 30(5): 963-967.?
Yang WY, Wang SN, Zhao WH, et al. Research of the glomerular ?filtration ?rate ?estimation ?model[J]. Journal of Biomedical Engineering, 2013, 30(5): 963-967.
[16] 高峰，吳曉東，周科平. 基于主成分分析和PSO-ELM算法的排土場穩(wěn)定性預(yù)測模型[J]. 黃金科學(xué)技術(shù)，2021, 29(5): 658-668.
Gao F, Wu XD, Zhou KP. Prediction model of soil dump stability based on principal component analysis and PSO-ELM algorithm[J]. Gold Science and Technology, 2021, 29(5): 658-668.
[17] 敬微微，韓倩，吳昊，等. 主成分分析和反向傳播神經(jīng)網(wǎng)絡(luò)模型在血液透析機預(yù)防維護中的應(yīng)用[J]. 中國醫(yī)學(xué)裝備, 2020, 17(7): 137-140.
Jing WW, Han Q, Wu H, et al. Application of PCA and BP neural network model in the preventive maintenance of hemodialysis machine[J]. China Medical Equipment, 2020, 17(7): 137-140.
[18] 吳定安，鐘建偉，王新磊，等. 主成分分析和長短期記憶網(wǎng)絡(luò)的電力負荷預(yù)測[J]. 物聯(lián)網(wǎng)技術(shù), 2021, 11(8): 47-51.
[19] 劉斌，李立欣，李靜. 一種改進的基于深度前饋神經(jīng)網(wǎng)絡(luò)的極化碼BP譯碼算法[J]. 移動通信，2019, 43(4): 8-14.
Liu B, Li LX, Li J. An improved polar BP decoding algorithm based on deep feedforward neural network[J]. Mobile Communications, 2019, 43(4): 8-14.
[20] 杜方洲. 基于深度前饋神經(jīng)網(wǎng)絡(luò)的TRMM降水產(chǎn)品降尺度研究[D]. 南京: 南京信息工程大學(xué)，2020.
Du FZ. Downscaling of TRMM precipitation products based on deep feedforward neural network[D]. ?Nanjing: Nanjing University of Information Science and Technology, 2020.
[21] Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine[J]. Nephron, 1976, 16(1): 31-41

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