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的损失在170亿至290亿美元之间。临床实验室提供了用于做出临床决策的大约70%的数据，在美国估计每年产生7000万个错误。目前检测临床实验室错误的方法可以改进。我们的假设是，贝叶斯方法将改进错误检测。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样数据作为本研究的模型。 这项建议的具体目标是：(1)构建和验证贝叶斯信念网络，旨在检测临床实验室值中的错误。对我们的前期工作进行扩展，以包括影响测量值的其他因素。为了实现这一目标，我们将从一项调查糖尿病治疗的随机对照试验中提取并清理数据集。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向每个数据集插入错误，并使用10次交叉验证从训练数据验证贝叶斯信任网络。通过改变用于将数据分类为错误的概率阈值，我们将确定网络的敏感度和特异度以及接收器操作特征曲线下的区域。最后，将在灵敏度、特异度和接收器操作特性曲线下区域之间的(z-关键)统计差异的度量上比较网络和人类专家的性能。(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中的网络结构在仅使用参数学习的情况下是否有效地在更一般的数据集中检测实验室错误。我们将对糖尿病前期和非糖尿病数据集中的每个学习到的网络参数估计进行10倍交叉验证。我们将确定网络在每个数据集中的敏感度和特异度，以及接收器工作特性曲线下区域之间的(z-关键)统计差异。最后，网络专家和人类专家的表现将在上述指标上进行比较。该项目与健康相关，其目标是减少临床实验室错误，这些错误可能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开发一种方法，临床实验室可以使用它来检测实践中的错误并节省 包括生命和大量的卫生保健资源。通过减少临床实验室的差错，生命和 将节省大量的医疗资源。

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.