Detecting Errors in Blood Labs Using Bayesian Networks

使用贝叶斯网络检测血液实验室中的错误

• 批准号: 7210158
• 负责人: JASON N. DOCTOR
• 金额: $ 26.07万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7210158
• 关键词: Acquired Immunodeficiency Syndrome; Adverse event; Affect; American; Area; Bayesian Method; Belief; Blood; Blood specimen; Clinical; Clinical Trials; Data; Data Set; Detection; Development; Diabetes Mellitus; Diagnosis; Epidemic; Goals; Health; Healthcare; Human; Institute of Medicine (U.S.); Laboratories; Learning; Life; Measures; Medical; Medical Errors; Methods; Modeling; Performance; Population; Probability; Process; Randomized Controlled Trials; Receiver Operating Characteristics; Reporting; Research; Research Personnel; Resources; Seminal; Sensitivity and Specificity; Structure; Testing; Training; Validation; Work; computer based statistical methods; cost; design; diabetic; health care service utilization; improved; killings; malignant breast neoplasm; non-diabetic; programs; success; vehicular accident

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.

描述（由申请人提供）： 医学医学研究所关于医疗错误的报告强调了其识别的紧迫性。据估计，医疗错误每年损失美国170亿至290亿美元。临床实验室提供了大约70％用于做出临床决策的数据，并在美国目前的检测临床实验室错误的方法中估计每年产生7000万个错误。我们的假设是贝叶斯方法将改善错误检测。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错误时是否比实验室专家更准确。我们使用非糖尿病，糖尿病前和糖尿病临床试验血板数据作为这项研究的模型。 该提案的具体目的是：（1）构建和验证旨在检测临床实验室价值错误的贝叶斯信念网络。一个扩展我们的初步工作，包括影响测量值的其他因素。为了实现这一目标，我们将从调查糖尿病治疗的随机对照试验中提取和清洁数据集。我们将数据随机分为训练集和测试集，以类似于将它们自然渲染的方式插入每个数据集中，并使用10倍的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，我们将确定网络的敏感性和特异性以及接收器操作特性曲线下的区域。最后，将在接收器操作特征曲线下的敏感性，特异性和（Z-关键的）统计差异的量度上进行网络与专家绩效。 （2）确定AIM 1中的贝叶斯网络是否概括为前和非糖尿病人群。我们将测试AIM 1中的网络结构是否有效检测仅具有参数学习的更通用数据集中的实验室错误。我们将对在每个预糖尿病数据集中的每个网络参数估计值中进行10倍的交叉验证。我们将确定每个数据集中网络的敏感性和特异性，以及接收器操作特征曲线下区域之间的（Z-关键）统计差异。最后，将根据上述措施比较网络与人类专家绩效。该项目的与健康相关性是可以从减少临床实验室错误的目标上可以明显看出的，这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开发一种临床实验室可能用来检测错误并节省错误的方法 生活和大量的医疗资源。通过减少临床实验室，生活和 将节省大量的医疗资源。