Detecting Errors in Blood Labs Using Bayesian Networks

描述（由申请人提供）： 医学研究所关于医疗差错的开创性报告强调了识别医疗差错的紧迫性。据估计，医疗差错每年给美国造成170亿至290亿美元的损失。临床实验室提供了约70%的用于做出临床决策的数据，并在美国每年产生约7000万个错误。我们的假设是，贝叶斯方法将提高错误检测。该项目的长期目标是评估贝叶斯网络在检测临床实验室数据中的错误方面是否比实验室专家更准确。我们使用非糖尿病，糖尿病前期和糖尿病临床试验的血液面板数据作为本研究的模型。 本研究的具体目标是：（1）建立并验证一个贝叶斯信念网络，用于检测临床实验室值中的错误。它扩展了我们的初步工作，包括影响测量值的其他因素。为了实现这一目标，我们将从一项研究糖尿病治疗的随机对照试验中提取和清理数据集。我们将数据随机分为训练集和测试集，以类似于自然呈现的方式将错误插入每个数据集，并使用10倍交叉验证从训练数据中验证贝叶斯信念网络。通过改变用于将数据分类为错误的概率阈值，我们将确定网络的灵敏度和特异性以及接收器操作特征曲线下的面积。最后，将比较网络与人类专家的性能，衡量灵敏度、特异性和接收器操作特征曲线下区域之间的（z临界）统计差异。(2)确定目标1中的贝叶斯网络是否适用于糖尿病前期和非糖尿病人群。我们将测试目标1中的网络结构是否能有效地检测更一般的数据集中的实验室错误，而只需要参数学习。我们将对每个糖尿病前和非糖尿病数据集的学习网络参数估计值进行10倍交叉验证。我们将确定网络在每个数据集中的灵敏度和特异性，以及受试者工作特征曲线下区域之间的（z临界）统计差异。最后，网络与人类专家的性能将在上述措施进行比较。该项目的健康相关性是显而易见的，其目标是减少临床实验室错误，可能会对医疗保健接受者的健康产生不利影响。该项目的成功将导致临床实验室可能用于检测实践中的错误并节省成本的方法的发展。 生命和大量的卫生保健资源。通过减少临床实验室的错误， 将节省大量的保健资源。

DESCRIPTION (provided by applicant): The Institute of Medicine seminal report on medical errors highlighted the urgency of their identification. Medical errors are estimated to cost the U.S. between $17 billion and $29 billion a year. Clinical laboratories provide about 70% of the data used to make clinical decisions and produce an estimated 70 million errors per year in the U.S. Current methods for detecting clinical laboratory errors can be improved. Our hypothesis is that a Bayesian approach will improve error detection. The long-term objective of this project is to evaluate if Bayesian networks are more accurate than laboratory experts in detecting errors in clinical laboratory data. We use non-diabetic, pre-diabetic and diabetic clinical trial blood panel data as models for this research. The specific aims of this proposal are: (1) To construct and validate a Bayesian belief network designed to detect errors in the clinical laboratory values. One that expands on our preliminary work to include other factors that influence measured values. To accomplish this aim we will extract and clean a data set from a randomized controlled trial investigating diabetes treatments. We randomly split the data into a training set and a test set, insert errors into each data set in ways analogous to how they would be rendered naturally and validate a Bayesian belief network from the training data using a 10-fold cross validation. By varying the probability threshold used to classify data as erroneous, we will determine the sensitivity and specificity of the network as well as the area under the receiver operating characteristics curve. Finally, network vs. human expert performance will be compared on measures of sensitivity, specificity, and (z-critical) statistical differences between areas under the receiver operating characteristics curves. (2) To determine whether the Bayesian network in Aim 1 generalizes to pre- and non-diabetic populations. We will test whether the network structure in Aim 1, is effective in detecting laboratory errors in more general data sets with only parameter learning. We will perform a 10-fold cross-validation over learned network parameter estimates in each of a pre- and a non-diabetic data set. We will determine the sensitivity and specificity of the network in each data set as well as the (z-critical) statistical differences between areas under the receiver operating characteristics curve. Finally, network vs. human expert performance will be compared on the aforementioned measures. The project's health-relatedness is evident by its goal of reducing clinical laboratory errors that can adversely affect the health of healthcare recipients. The success of this project will result in the development of a method that clinical laboratories may use to detect errors in practice and save both lives and substantial health-care resources. By reducing errors in the clinical laboratory, lives and substantial health-care resources will be saved.