Mastitis in dairy production: Estimation of sensitivity, specificity and disease prevalence in the absence of a gold standard

Mastitis, a worldwide endemic disease of dairy cows, is an important cause of decreased efficiency in milk production. Early medical treatment can reduce the nonreversible losses in milk production caused by this infection. Various diagnostic tests for mastitis are available, including a test measuring the electrical conductivity of milk (MEC test), the industry standard of somatic cell counting (SCC test), a bacteriological test, and a recently developed test measuring mammary associated amyloid A (MAA test). None of these tests is considered a gold standard, however. The aim of the present study was to determine which of these tests provides the best results, and at what cost, to improve the efficiency of milk production. For this study, 25 cows were tested at all four quarters of the udder with each of the aforementioned mastitis diagnostic tests. Based on the data, the disease prevalence as well as the sensitivity and the specificity of the four tests were estimated with a Bayesian approach by extending the Hui and Walter model with two independent tests and two populations to a model with four partially dependent tests and one population. This model was further combined with a receiver operating characteristics analysis to estimate the overall test accurac

Evaluation methods and decision theory for classification of streaming data with temporal dependence

Predictive modeling on data streams plays an important role in modern data analysis, where data arrives continuously and needs to be mined in real time. In the stream setting the data distribution is often evolving over time, and models that update themselves during operation are becoming the state-of-the-art. This paper formalizes a learning and evaluation scheme of such predictive models. We theoretically analyze evaluation of classifiers on streaming data with temporal dependence. Our findings suggest that the commonly accepted data stream classification measures, such as classification accuracy and Kappa statistic, fail to diagnose cases of poor performance when temporal dependence is present, therefore they should not be used as sole performance indicators. Moreover, classification accuracy can be misleading if used as a proxy for evaluating change detectors with datasets that have temporal dependence. We formulate the decision theory for streaming data classification with temporal dependence and develop a new evaluation methodology for data stream classification that takes temporal dependence into account. We propose a combined measure for classification performance, that takes into account temporal dependence, and we recommend using it as the main performance measure in classification of streaming data

Dynamic Bayeian Inference Networks and Hidden Markov Models for Modeling Learning Progressions over Multiple Time Points

The current study examines the performance of a Bayesian Inference Network (BIN) for modeling Learning Progressions (LP) as a longitudinal design approach. Recently, Learning Progressions, defined by measurable pathways that a student may follow in building their knowledge and gaining expertise over time (National Research Council, 2007; Shin, Stevens, Short & Krajcik, 2009), have captured attention in mathematics and science education (Learning Progressions in Science Conference, 2009). While substantive, psychological, instructional, and task developmental aspects has been proposed in the LP framework, few assessment design frameworks have been designed to link the theory embodied in a progression, tasks that provide evidence about a student's level on that progression, and psychometric models that can link them. Specially, few psychometric models have been proposed to characterize the relationship between student performance and levels on learning progressions in a longitudinal design approach. This dissertation introduces an approach to modeling LPs over multiple time points using Bayesian Inference Networks, referred to as dynamic Bayesian Inference Networks (DBINs). The DBINs are a framework for modeling LPs over time by integrating the theory embodying LPs, assessment design, and interpretation of student performances. The technical aspects of this dissertation cover the fundamental concepts of the graphical model for constructing a DBIN. It is shown that this modeling strategy for change over multiple time points is equivalent to a hidden Markov model. An expectation-maximization (EM) algorithm is presented for estimating the parameters in the model. Two simulation studies are conducted that focus on the construction of a simple DBIN model and an expanded DBIN model with a covariate. The extension that incorporates a covariate for students is useful for studying the effect of instructional treatments, students' background, and motivation on a student's LP. An application illustrates the ideas with real data from the domain of beginning computer network engineering drawn from work in the Cisco Networking Academy