USE OF NEURAL NETWORKS IN MONITORING QUALITY CONTROL OF LAB TESTS

该项目重点关注神经网络在以下方面的有用性 监测患者实验室数据以进行质量控制。我们开发 一个神经网络，用病人的血清总量数据训练它， 胆固醇我们还用数学方法训练了神经网络 不符合我们标准质量控制程序的模拟数据。 经过训练，神经网络能够识别通常的 我们实验室的胆固醇结果模式， 将其与不正确的实验室结果模式区分开来 是化学分析仪出了问题神经 网络能够识别偏差和精度方面的问题 在实验室数据中，使用小至20名患者的样本量。 受试者操作特征分析表明， 网络法在敏感性和误差特异性方面均具有上级优势 与其他方法相比。研究报告的手稿 正在筹备中未来的研究将扩展神经网络 接近其他分析物，并将神经网络与 一个化学分析仪来测试这种方法的实用性， 监测质量控制。

This project focused on the usefulness of neural networks in monitoring patient laboratory data for quality control. We developed a neural network by training it with patient data on total serum cholesterol. We also trained the neural network with mathematically simulated data that failed our standard quality control procedures. After training, the neural network was able to recognize the usual pattern of cholesterol results in our laboratory and to differentiate it from a pattern of incorrect laboratory results produced by a problem with the chemistry analyzer. The neural network was able to recognize problems in bias, as well as precision in the laboratory data, using a sample size as small as 20 patients. Receiver operating characteristic analysis showed that the neural network approach is superior in sensitivity and specificity in error detection compared with other approaches. A manuscript of the study is being prepared. Future studies will extend the neural network approach to other analytes and will interface a neural network with a chemistry analyzer to test the practicality of this approach in monitoring quality control.