Validating Variational Bayes Linear Regression Method With Multi-Central Datasets.
Validating Variational Bayes Linear Regression Method With Multi-Central Datasets.
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DOI：
10.1167/iovs.17-22907
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发表时间：
2018-04-01
影响因子：
4.4
通讯作者：
Asaoka R
中科院分区：
文献类型：
--
作者：
Murata H;Zangwill LM;Fujino Y;Matsuura M;Miki A;Hirasawa K;Tanito M;Mizoue S;Mori K;Suzuki K;Yamashita T;Kashiwagi K;Shoji N;Asaoka R
为了验证变分贝叶斯线性回归（VBLR）的预测精度与两个数据集外部的训练数据集。训练数据集由来自东京大学医院的4278名受试者的7268只眼睛组成。日本青光眼多中心数据库档案（JAMDIG）数据集包括177例患者的271只眼，青光眼诊断创新研究（DIGS）数据集包括173例患者的248只眼，用于验证。预测精度之间的VBLR和普通最小二乘线性回归（OLSLR）进行了比较。首先，使用JAMDIG和DIGS数据集中每个患者的第二至第四视野（VF）（VF 2 -4）至第二至第十视野（VF 2 -10）的52个测试点中的每个测试点的总偏差（TD）值进行OLSLR和VBLR，并且每次预测第十一视野测试的TD值。通过均方根误差（RMSE）统计量比较了每种方法的预测精度。使用JAMDIG和DIGS数据集的OLSLR RMSE在31和4.3 dB之间，在19.5和3.9 dB之间。另一方面，JAMDIG和DIGS数据集的VBLR RMSE在5.0和3.7之间，在4.6和3.6 dB之间。对于每个系列（VF 2 -4至VF 2 -10）的两个数据集，VBLR和OLSLR之间存在统计学显著差异（所有测试均为P < 0.01）。然而，在任何VF系列（VF 2 -2至VF 2 -10）下，JAMDIG和DIGS数据集之间的VBLR RMSE均无统计学显著差异（P > 0.05）。VBLR在预测未来VF进展方面优于OLSLR，并且VBLR有可能成为临床环境中的有用工具。
To validate the prediction accuracy of variational Bayes linear regression (VBLR) with two datasets external to the training dataset. The training dataset consisted of 7268 eyes of 4278 subjects from the University of Tokyo Hospital. The Japanese Archive of Multicentral Databases in Glaucoma (JAMDIG) dataset consisted of 271 eyes of 177 patients, and the Diagnostic Innovations in Glaucoma Study (DIGS) dataset includes 248 eyes of 173 patients, which were used for validation. Prediction accuracy was compared between the VBLR and ordinary least squared linear regression (OLSLR). First, OLSLR and VBLR were carried out using total deviation (TD) values at each of the 52 test points from the second to fourth visual fields (VFs) (VF2–4) to 2nd to 10th VF (VF2–10) of each patient in JAMDIG and DIGS datasets, and the TD values of the 11th VF test were predicted every time. The predictive accuracy of each method was compared through the root mean squared error (RMSE) statistic. OLSLR RMSEs with the JAMDIG and DIGS datasets were between 31 and 4.3 dB, and between 19.5 and 3.9 dB. On the other hand, VBLR RMSEs with JAMDIG and DIGS datasets were between 5.0 and 3.7, and between 4.6 and 3.6 dB. There was statistically significant difference between VBLR and OLSLR for both datasets at every series (VF2–4 to VF2–10) (P < 0.01 for all tests). However, there was no statistically significant difference in VBLR RMSEs between JAMDIG and DIGS datasets at any series of VFs (VF2–2 to VF2–10) (P > 0.05). VBLR outperformed OLSLR to predict future VF progression, and the VBLR has a potential to be a helpful tool at clinical settings.
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影响因子：
4.4
作者：
Fujino, Yuri;Asaoka, Ryo;Shoji, Nobuyuki
通讯作者：
Shoji, Nobuyuki
影响因子：
4.1
作者：
Fitzke, FW;Hitchings, RA;Crabb, DP
通讯作者：
Crabb, DP
影响因子：
4.2
作者：
DRANCE, SM;DOUGLAS, GR;HITCHINGS, RA
通讯作者：
HITCHINGS, RA
影响因子：
4.2
作者：
CHAUHAN, BC;DRANCE, SM;JOHNSON, CA
通讯作者：
JOHNSON, CA
影响因子：
4.6
作者：
Japanese Archive of Multicentral Database in Glaucoma (JAMDIG) construction group
通讯作者：
Japanese Archive of Multicentral Database in Glaucoma (JAMDIG) construction group