Establishment of a real-time quality control method for identifying random error in serum sodium ion based on artificial intel-ligence voting algorithm
10.13602/j.cnki.jcls.2024.10.11
- VernacularTitle:基于人工智能投票算法建立识别血清钠离子随机误差的实时质量控制法
- Author:
Yuan LIU
1
;
Hexiang ZHENG
;
Zhiye XU
;
Wenqin CHEN
;
Hongyan SONG
;
Yuxin CHEN
Author Information
1. 南京大学医学院附属鼓楼医院检验科,南京 210008
- Keywords:
quality control;
patient data;
real-time quality control;
random error;
artificial intelligence
- From:
Chinese Journal of Clinical Laboratory Science
2024;42(10):772-777
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a novel real-time quality control method for rapidly identifying the random error of sodium ion con-centration in serum using an artificial intelligence voting algorithm,and evaluate the relevant effectiveness of the model established on this basis.Methods A total of 144 754 test results of serum sodium ion rom the inpatients measured by Beckman AU5400 biochemis-try analyzer from January to May 2021 were obtained retrospectively from laboratory information system of the Department of Clinical La-boratory,Nanjing Drum Tower Hospital,and all the data were used as unbiased data for the current study.The random errors were arti-ficially introduced to generate the corresponding biased data set.Subsequently,the voting algorithm-based internal quality control model(ViQC)was established using the principles of the voting algorithm.The ViQC model and five classical PBRTQC(patient-based real-time quality control)algorithms were performed direct to each biased data.The analytical performance of the ViQC model was evaluated by using classification model criteria.The trimmed average number of patient samples until error detection(tANPed)was used to com-pare the clinical detection efficacy of the ViQC model with those of the five classical algorithms,and the error detection curves were plotted.Results Compare with all the classical algorithms,the ViQC model showed a false positive rate below 0.002 and achieved ac-curacy above 0.951 in detecting all the deviations.When the error factors were 1.5,2.5,and 3.0,the false positive rate of the ViQC model was zero.When the error factor was 2.5,its accuracy reached 0.979.Compared to the five classical PBRTQC algorithms,the ViQC model reduced the overall average tANPed by up to 34％and showed higher sensitivity for error detection.In addition,the ViQC model demonstrated the area under the ROC curve was as high as 0.989 at TEa on the test set,but the value of tANPed wasonly five.Conclusion We successfully established a real-time quality control model for the data of patients based on artificial intelligence algo-rithms,and its efficacy of clinical detection was superior to the traditional PBRTQC algorithms.
