The duration of children stay in a temporary foster care system needs to be minimal in order to ensure a stable and successful life. However, a time-consuming procedure of investigations is usually taken to decide whether they can reunite with their birth parents. Moreover, if the child fails to reunite with their family, another discharge decision needs to be assessed, leading to even longer time without a normal life. Based on the data from Adoption and Foster Care Analysis and Reporting System (AFCARS), this thesis derives a prediction model to discriminate the children with a tendency of unsuccessful reunification from the rest. An alternative discharge option can therefore be prepared concurrently for the foster youth with high non-reunification probability. The model is obtained by logistic regression and evaluated with receiver operating characteristic (ROC) curve.

In diagnostic test studies, one crucial task is to evaluate the diagnostic accuracy of a test. Currently, most studies focus on the Receiver Operating Characteristics Curve and the Area Under the Curve. On the other hand, the Youden index, widely applied in practice, is another comprehensive measurement for the performance of a diagnostic test. For a continuous-scale test classifying diseased and non-diseased groups, finding the Youden index of the test is equivalent to maximize the sum of sensitivity and specificity for all the possible values of the cut-point. This dissertation concentrates on statistical inferences for the Youden index. First, an auxiliary tool for the Youden index, called the diagnostic curve, is defined and used to evaluate the diagnostic test. Second, in the paired-design study to assess the diagnostic accuracy of two biomarkers, the difference in paired Youden indices frequently acts as an evaluation standard. We propose an exact confidence interval for the difference in paired Youden indices based on generalized pivotal quantities. A maximum likelihood estimate-based interval and a bootstrap-based interval are also included in the study. Third, for certain diseases, an intermediate level exists between diseased and non-diseased status. With such concern, we define the Youden index for three ordinal groups, propose the empirical estimate of the Youden index, study the asymptotic properties of the empirical Youden index estimate, and construct parametric and nonparametric confidence intervals for the Youden index. Finally, since covariates often affect the accuracy of a diagnostic test, therefore, we propose estimates for the Youden index with a covariate adjustment under heteroscedastic regression models for the test results. Asymptotic properties of the covariate-adjusted Youden index estimators are investigated under normal error and non-normal error assumptions.

Diagnostic tests are central in the field of modern medicine. One of the main factors for interpreting a diagnostic test is the discriminatory accuracy. For a continuous-scale diagnostic test, the area under the receiver operating characteristic (ROC) curve, AUC, is a useful one-number summary index for the diagnostic accuracy of the test. When only a particular region of the ROC curve would be of interest, the partial AUC (pAUC) is a more appropriate index for the diagnostic accuracy. In this thesis, we develop seven confidence intervals for the pAUC under the semi-parametric models for the diseased and non-diseased populations by using the normal approximation, bootstrap and empirical likelihood methods. In addition, we conduct simulation studies to compare the finite sample performance of the proposed confidence intervals for the pAUC. A real example is also used to illustrate the application of the recommended intervals.

Customer targeting is the key to the success of bank telemarketing. To compare the flexible discriminant analysis and the logistic regression in customer targeting, a survey dataset from a Portuguese bank was used. For the flexible discriminant analysis model, the backward elimination of explanatory variables was used with several rounds of manual re-defining of dummy variables. For the logistic regression model, the automatic stepwise selection was performed to decide which explanatory variables should be left in the final model. Ten-fold stratified cross validation was performed to estimate the model parameters and accuracies. Although employing different sets of explanatory variables, the flexible discriminant analysis model and the logistic regression model show equally satisfactory performances in customer classification based on the areas under the receiver operating characteristic curves. Focusing on the predicted “right” customers, the logistic regression model shows slightly better classification and higher overall correct prediction rate.

Šiame darbe yra nagrinėjamas piršto antspaudo atpažinimo uždavinio viena iš sudedamųjų dalių: skaitmeninės informacijos apie piršto antspaudą gavimas. Aprašomas metodas, paremtas kreivių kreivumų įvertinimu bei minimalaus žiedo sąvoka. Taip pat, aprašytas naujas Delaunė trianguliacijos radimo, minimalaus žiedo skaičiavimo bei kreivių kreivumų įvertinimo algoritmai. Darbo tikslas - pagerinti piršto antspaudo atpažinimo algoritmo kokybę, bei greitį. / The recognition of fingerprint is discussed in this article. The goal of this work is to increase the quality of fingerprint recognition method, and to improve algorithm speed. The new method of fingerprint data for fingerprint matching is analyzed. It concentrates on calculating values of curve curvatures, and minimum-width annuli. Some new methods of evaluation of this properties are described step by step.

No description available.

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (p. 173-178).

abstract: The Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) General Abilities Index (GAI) and Cognitive Proficiency Index (CPI) have been advanced as possible diagnostic markers of Attention-Deficit Hyperactivity Disorder (ADHD). Diagnostic utility statistics were used to test the ability of GAI-CPI difference scores to identify children with ADHD. Participants included an ADHD sample (n = 78), a referred but non-diagnosed hospital sample (n = 66), and a simulated sample with virtually identical psychometric characteristics as the WISC-IV 2,200 child standardization sample. Receiver Operating Characteristic (ROC) analyses were computed to determine the utility of GAI-CPI difference scores to identify children with ADHD. The GAI-CPI discrepancy method had an AUC of .64, 95% CI [0.58, 0.71] for the ADHD sample compared to the simulated normative sample and an AUC of .46, 95% CI [0.37, 0.56] for the ADHD sample compared to the referred but non-diagnosed hospital sample. These AUC scores indicate that the GAI-CPI discrepancy method has low accuracy. / Dissertation/Thesis / M.A. Educational Psychology 2010

INTRODUCTION: Abdominal pain and diarrhea are two of the most common pediatric complaints. They are often associated with a diagnosis of Crohn Disease or Ulcerative Colitis, collectively known as inflammatory bowel disease (IBD). IBD is set of diseases with ill-defined pathogenesis but similar clinical presentation. Clinicians rely on colonoscopic evaluation to distinguish between the two disorders, and the rate of colonoscopies has been increasing over the past several years. With the risks and costs associated with colonoscopic evaluation, our study sought to identify physiologic variables with significant predictive value in order to better determine those most likely to have an abnormal colonoscopy. Those variables could then be incorporated into a predictive model to stratify the risk of a patient having an abnormal colonoscopy and be used as a decision assist tool for physicians. METHODS: We conducted a retrospective cohort study examining 443 patients who underwent a colonoscopy between the years of 2012 and 2016 at Boston Children’s Hospital. Data on demographics, lab work, and stool studies was collected into an online database for three separate data sets. It was analyzed using SAS 9.4 and logistic regression was performed to identify four variables with the most predictive value relating to abnormal colonoscopy. Those variables were incorporated into a predictive model. RESULTS: Several variables were determined to be statistically significant in the prediction of abnormal colonoscopy. The four variables with the most predictive value based on calculated odds ratios were family history of IBD in a first-degree relative, serum albumin, fecal lactoferrin, and platelet count. When ROC curves were generated to validate the model using the four variables for each of the data sets, the area under the ROC curve was used to assess the robustness of the predictive model. The area under the curve (AUC) for the training data set was .81, the first validation set was .79, and the second validation set was .6. DISCUSSION: ROC curves were generated for each of the data sets in order to assess the predictive ability of the model, and the AUCS were calculated. An AUC of 1.0 would indicate a predictive model with perfect predictability. The AUC of the model building set at .81 and the first validation set at .79 are indicative of a predictive model with strong predictive value. The second validation set, used to assess the success of the model on an external data set, had an AUC of .6, which is less robust in its predictive value but is of more predictive utility than a coin flip. CONCLUSION: Logistic regression yielded a parsimonious model consisting of four variables with the strongest predictive value in terms of having an abnormal colonoscopy. The variables are metrics that are routinely collected as part of ambulatory and inpatient clinic visits. When the model was validated using an external data set, it did not perform as well as expected based on the results of the training and first validation set. If the robustness of the model can be improved when validated using an external data set, it could be of great clinical utility to physicians as a decision assist tool and help to limit the number of less clinically indicated colonoscopies being performed in the future.

Made available in DSpace on 2015-12-06T23:46:16Z (GMT). No. of bitstreams: 0 Previous issue date: 2013 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Introdução: A obesidade, como um grave problema de Saúde publica, vem sendo vista atualmente como uma epidemia mundial. Diante disso, varios indicadores antropometricos foram criados para detectar e prevenir precocemente a obesidade. Objetivo: Verificar o poder discriminativo do indice de massa corporal (IMC) para classificar diferentes niveis de obesidade em individuos do sexo masculino. Em paralelo, sugerir e comparar com o IMC uma nova formula para estimar diretamente o percentual de gordura corporal (%G). Metodos: O estudo foi composto por 5072 avaliacoes fisicas realizadas em individuos do sexo masculino, entre os anos de 2002 e 2004, em clubes recreativos na Grande São Paulo. As avaliacoes foram separadas de forma randomizada em dois grupos. No primeiro grupo, denominado Regressao foi realizada uma regressao linear para o desenvolvimento de um novo indicador antropometrico. No segundo grupo, denominado Teste, foi aplicada curva ROC para verificar o poder discriminativo e determinar os pontos de corte com suas respectivas sensibilidades e especificidades. Foi verificado se ocorre diferenca significante entre as areas sob a curva ROC do IMC e IMG, tambem foi utilizada correlacao de Pearson e analise descritiva. O nivel de significancia adotado nos testes foi de 5%. Resultados: Os resultados da regressao linear foram: (Indice de massa gorda) IMG = 0,19 (Idade) + 0,39 (peso) - 9,36 (estatura). O poder discriminativo do IMC variou entre 0,7030 e 0,7925, ja o IMC variou entre 0,6908 e 0,7747, para os diferentes niveis de obesidades estudados. As comparacoes entre as areas sob as curvas ROC demonstraram que nao houve diferenca significativa entre o poder discriminativo do IMC e o IMG. As correlacoes de Pearson entre %G x IMC e %G x IMG foram de 0,7684 e 0,7708, respectivamente. Conclusao: O IMC apresentou valores dos pontos de corte abaixo dos propostos pela Organizacao Mundial de Saúde (OMS) para classificacao de diferentes niveis de obesidade. Nao houve diferenca significativa nas areas abaixo da curva ROC entre o IMC e IMG / BV UNIFESP: Teses e dissertações