■我们比较了人工智能-基于患者的实时质量控制(AI-PBRTQC)和传统的PBRTQC在实验室中的质量控制效率,为PBRTQC在临床实验室中的更广泛的应用创造了有利条件。
■在本研究中,总甲状腺素(TT4)患者的数据,抗苗勒管激素(AMH),丙氨酸氨基转移酶(ALT),总胆固醇(TC),尿素,和白蛋白(ALB)超过5个月分为两组:AI-PBRTQC组和传统PBRTQC组。Box-Cox变换方法估计了常规PBRTQC组中的截断范围。相比之下,在AI-PBRTQC组中,PBRTQC软件平台智能选择截断范围。我们通过结合不同的加权因子开发了各种验证模型,表示为λ。错误检测,假阳性率,假阴性率,直到错误检测的患者样本的平均数量,和曲线下面积用于评估本研究中的最佳PBRTQC模型。本研究通过分析质量风险案例,为AI-PBRTQC在识别质量风险方面的有效性提供了证据。
■PBRTQC的最佳参数设置方案是TT4(78-186),λ=0.03;AMH(0.02-2.96),λ=0.02;ALT(10-25),λ=0.02;TC(2.84-5.87),λ=0.02;尿素(3.5-6.6),λ=0.02;ALB(43-52),λ=0.05。
■AI-PBRTQC组在识别质量风险方面比常规PBRTQC更有效。AI-PBRTQC还可以有效识别少量样品中的质量风险。AI-PBRTQC可用于确定生物化学和免疫学分析物的质量风险。AI-PBRTQC识别质量风险,如试剂校准,船上时间,和品牌变化。
UNASSIGNED: We compared the quality control efficiency of artificial intelligence-patient-based real-time quality control (AI-PBRTQC) and traditional PBRTQC in laboratories to create favorable conditions for the broader application of PBRTQC in clinical laboratories.
UNASSIGNED: In the present
study, the data of patients with total thyroxine (TT4), anti-Müllerian hormone (AMH), alanine aminotransferase (ALT), total cholesterol (TC), urea, and albumin (ALB) over five months were categorized into two groups: AI-PBRTQC group and traditional PBRTQC group. The Box-Cox transformation method estimated truncation ranges in the conventional PBRTQC group. In contrast, in the AI-PBRTQC group, the PBRTQC software platform intelligently selected the truncation ranges. We developed various validation models by incorporating different weighting factors, denoted as λ. Error detection, false positive rate, false negative rate, average number of the patient sample until error detection, and area under the curve were employed to evaluate the optimal PBRTQC model in this
study. This
study provides evidence of the effectiveness of AI-PBRTQC in identifying quality risks by analyzing quality risk cases.
UNASSIGNED: The optimal parameter setting scheme for PBRTQC is TT4 (78-186), λ = 0.03; AMH (0.02-2.96), λ = 0.02; ALT (10-25), λ = 0.02; TC (2.84-5.87), λ = 0.02; urea (3.5-6.6), λ = 0.02; ALB (43-52), λ = 0.05.
UNASSIGNED: The AI-PBRTQC group was more efficient in identifying quality risks than the conventional PBRTQC. AI-PBRTQC can also effectively identify quality risks in a small number of samples. AI-PBRTQC can be used to determine quality risks in both biochemistry and immunology analytes. AI-PBRTQC identifies quality risks such as reagent calibration, onboard time, and brand changes.