Establishment and evaluation of a method for identifying the random error in the quantitative measurement procedure based on back propagation neural network
10.3760/cma.j.cn114452-20210921-00590
- VernacularTitle:基于反向传播神经网络算法建立识别定量测量程序随机误差的方法及性能评价
- Author:
Yufang LIANG
1
;
Huarong ZHENG
;
Zhe WANG
;
Xiang FENG
;
Zewen HAN
;
Biao SONG
;
Huali CHENG
;
Qingtao WANG
;
Rui ZHOU
Author Information
1. 首都医科大学附属北京朝阳医院检验科，北京 100020
- Keywords:
Artificial intelligence;
Back propagation neutral network;
Random error;
Real-time quality control;
Patient data
- From:
Chinese Journal of Laboratory Medicine
2022;45(5):543-548
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish and evaluate a new real-time quality control method that can identify the random errors by using the backpropagation neural network (BPNN) algorithm and taking blood glucose test as an example.Methods:A total of 219 000 blood glucose results measured by Siemens advia 2 400 analytical system from January 2019 to July 2020 and derived from Laboratory Information System of Beijing Chaoyang Hospital Laboratory Department was regarded as the unbiased data of our study. Six deviations with different sizes were introduced to generate the corresponding biased data. With each biased data, BPNN and MovSD algorithms were used and tested, and then evaluated by traceability method and clinical method.Results:For BPNN algorithm, the block size was pre-set to 10 and the false-positive rate in all biases was within 0.1%. For MovSD, however, the optimal block size and exclusive limit were 150 and 10% separately and its false-positive rate in all biases was 0.38%, which was 0.28% higher than BPNN. Especially, for the least two error factors of 0.5 and 1, all the random errors were not detected by MovSD; for the error factor larger than 1, random errors could be detected by MovSD but the MNPed was higher than that of BPNN under all deviations. The difference was up to 91.67 times. 460 000 reference data were produced by traceability procedure. The uncertainty of BPNN algorithm evaluated by these reference data was only 0.078%.Conclusion:A real-time quality control method based on BPNN algorithm was successfully established to identify random errors in analytical phase, which was more efficient than MovSD method and provided a new idea and method for the identification of random errors in clinical practice.
