Abstract：
BACKGROUND: Patient-Based Real-Time Quality Control (PBRTQC) has emerged as a supplementary programme to traditional internal quality control (iQC) mechanisms. Despite its growing popularity, practical applications in clinical settings reveal several challenges. The primary objective of this research is to introduce and develop an Artificial Intelligence (AI)-based method, named Voting algorithm based iQC (ViQC), designed to enhance the precision and reliability of existing PBRTQC systems.
METHODS: In this study, we conducted a retrospective analysis of 111,925 inpatient serum glucose test results from Nanjing Drum Tower Hospital, Nanjing, China, to provide an unbiased data set. The Voting iQC (ViQC) algorithm, established by the principles of the Voting algorithm, was then developed. Its analytical performance was evaluated through the calculation of random errors (RE). Subsequently, its clinical efficacy was assessed by comparison with five statistical algorithms: Moving Average (MA), Exponentially Weighted Moving Average (EWMA), Moving Median (movMed, MM), Moving Quartile (MQ), and Moving Standard Deviation (MovSD).
RESULTS: The ViQC model incorporates a variety of machine learning models, including logistic regression, Bayesian methods, K-Nearest Neighbor, decision trees, random forests, and gradient boosting decision trees, to establish a robust predictive framework. This model consistently maintains a false positive rate below 0.002 across all six evaluated error factors, showcasing exceptional precision. Notably, its performance further excels with an error factor of 3.0, where the false positive rate drops below 0.001, and achieves an accuracy rate as high as 0.965 at an error factor of 2.0. The classification effectiveness of ViQC model is evaluated by an area under the curve (AUC) exceeding 0.97 for all error factors. In comparison to five conventional PBRTQC statistical methods, ViQC significantly enhances error detection efficiency, maximum reducing the trimmed average number of patient samples required for detecting errors from 724 to 168, thereby affirming its superior error detection capability.
CONCLUSIONS: The new established PBRTQC using artificial intelligence yielded satisfactory performance compared to the traditional PBBTQC in real world setting.
METHODS: In this study, we conducted a retrospective analysis of 111,925 inpatient serum glucose test results from Nanjing Drum Tower Hospital, Nanjing, China, to provide an unbiased data set. The Voting iQC (ViQC) algorithm, established by the principles of the Voting algorithm, was then developed. Its analytical performance was evaluated through the calculation of random errors (RE). Subsequently, its clinical efficacy was assessed by comparison with five statistical algorithms: Moving Average (MA), Exponentially Weighted Moving Average (EWMA), Moving Median (movMed, MM), Moving Quartile (MQ), and Moving Standard Deviation (MovSD).
RESULTS: The ViQC model incorporates a variety of machine learning models, including logistic regression, Bayesian methods, K-Nearest Neighbor, decision trees, random forests, and gradient boosting decision trees, to establish a robust predictive framework. This model consistently maintains a false positive rate below 0.002 across all six evaluated error factors, showcasing exceptional precision. Notably, its performance further excels with an error factor of 3.0, where the false positive rate drops below 0.001, and achieves an accuracy rate as high as 0.965 at an error factor of 2.0. The classification effectiveness of ViQC model is evaluated by an area under the curve (AUC) exceeding 0.97 for all error factors. In comparison to five conventional PBRTQC statistical methods, ViQC significantly enhances error detection efficiency, maximum reducing the trimmed average number of patient samples required for detecting errors from 724 to 168, thereby affirming its superior error detection capability.
CONCLUSIONS: The new established PBRTQC using artificial intelligence yielded satisfactory performance compared to the traditional PBBTQC in real world setting.
摘要：
背景：基于患者的实时质量控制（PBRTQC）已成为传统内部质量控制（iQC）机制的补充程序。尽管它越来越受欢迎，在临床环境中的实际应用揭示了几个挑战。这项研究的主要目的是引入和开发一种基于人工智能(AI)的方法，基于iQC的投票算法(ViQC)，旨在提高现有PBRTQC系统的精度和可靠性。
方法：在本研究中，我们对南京鼓楼医院111,925例住院患者血糖检测结果进行了回顾性分析,南京,中国,提供无偏数据集。投票iQC（ViQC）算法，由投票算法的原理建立，然后开发。通过计算随机误差（RE）来评估其分析性能。随后，通过与五种统计学算法进行比较来评估其临床疗效：移动平均(MA)，指数加权移动平均(EWMA)，移动中值(移动中值,MM),移动四分位数（MQ），和移动标准偏差（MovSD）。
结果：ViQC模型融合了多种机器学习模型，包括逻辑回归，贝叶斯方法，K-最近的邻居,决策树,随机森林，和梯度增强决策树，建立一个稳健的预测框架。该模型在所有六个评估的误差因素中始终保持低于0.002的假阳性率，展示卓越的精度。值得注意的是,其性能进一步优于3.0的误差因子，其中假阳性率降至0.001以下，并在2.0的误差因子下达到高达0.965的准确率。ViQC模型的分类有效性通过对于所有误差因素超过0.97的曲线下面积(AUC)来评估。与五种常规PBRTQC统计方法相比，ViQC显著提高了错误检测效率，将检测错误所需的患者样本的平均数量从724减少到168，从而肯定了其卓越的错误检测能力。
结论：与现实环境中的传统PBBTQC相比，使用人工智能的新建立的PBRTQC产生了令人满意的性能。
方法：在本研究中，我们对南京鼓楼医院111,925例住院患者血糖检测结果进行了回顾性分析,南京,中国,提供无偏数据集。投票iQC（ViQC）算法，由投票算法的原理建立，然后开发。通过计算随机误差（RE）来评估其分析性能。随后，通过与五种统计学算法进行比较来评估其临床疗效：移动平均(MA)，指数加权移动平均(EWMA)，移动中值(移动中值,MM),移动四分位数（MQ），和移动标准偏差（MovSD）。
结果：ViQC模型融合了多种机器学习模型，包括逻辑回归，贝叶斯方法，K-最近的邻居,决策树,随机森林，和梯度增强决策树，建立一个稳健的预测框架。该模型在所有六个评估的误差因素中始终保持低于0.002的假阳性率，展示卓越的精度。值得注意的是,其性能进一步优于3.0的误差因子，其中假阳性率降至0.001以下，并在2.0的误差因子下达到高达0.965的准确率。ViQC模型的分类有效性通过对于所有误差因素超过0.97的曲线下面积(AUC)来评估。与五种常规PBRTQC统计方法相比，ViQC显著提高了错误检测效率，将检测错误所需的患者样本的平均数量从724减少到168，从而肯定了其卓越的错误检测能力。
结论：与现实环境中的传统PBBTQC相比，使用人工智能的新建立的PBRTQC产生了令人满意的性能。
