A Novel Correction for the Adjusted Box-Pierce Test — New Risk Factors for Emergency Department Return Visits within 72 hours for Children with Respiratory Conditions — General Pediatric Model for Understanding and Predicting Prolonged Length of Stay

This thesis represents the results of three research projects that underline the breadth and depth of my interests. Firstly, I devoted some efforts to the well-known Box-Pierce goodness-of-fit tests for time series models which has been an important research topic over the last few decades. All previously proposed tests are focused on changes of the test statistics. Instead, I adopted a different approach that takes the best performing test and modifying the rejection region. Thus, I developed a semiparametric correction of the Adjusted Box-Pierce test that attains the best I error rates for all sample sizes and lags and outperforms all previous global time series goodness-of-fit approaches. Secondly, I aimed to study and identify novel risk factors significantly associated with 72-hour return visits to emergency departments. I queried data consisting of 185,000 ED visits of patients less than 18 years in the United States using the Cerner® Health Facts Database. A nested mixed-effects logistic regression model to provide statistical inference on associated risk factors was built, and a representative set of machine learning algorithms for our predictive modeling task was selected. New respiratory conditions including acute bronchiolitis, pneumonia, and asthma were identified as risk factors for return visits to ED. Thirdly, I ambitioned to design and implement a comprehensive study to identify new clinical and demographic factors associated with prolonged length of stay ($\u3e$ two weeks) among pediatric patients (aged 18 years and under) in a number of free-standing pediatric and mixed medical facilities. I implemented a mixed effect model to assess the statistical significance and effect sizes of age, race/ethnicity, number of medications, medical family history, presence of infection agents (fungi, bacteria, virus), cancer diagnoses, and other conditions as well as some clinical variables. A stochastic gradient model was also implemented for prediction. From the mixed-effects model, 11 main effect predictors were found to be significantly and statistically associated with an increase in the odds of prolonged length of stay. The area under the operator characteristic curve (AUROC) for the mixed-effects model was 0.887 (0.885, 0.889) and the extreme gradient boosting model attained an AUROC of 0.931 (0.930, 0.933)

A Novel Correction for the Adjusted Box-Pierce Test

The classical Box-Pierce and Ljung-Box tests for auto-correlation of residuals possess severe deviations from nominal type I error rates. Previous studies have attempted to address this issue by either revising existing tests or designing new techniques. The Adjusted Box-Pierce achieves the best results with respect to attaining type I error rates closer to nominal values. This research paper proposes a further correction to the adjusted Box-Pierce test that possesses near perfect type I error rates. The approach is based on an inflation of the rejection region for all sample sizes and lags calculated via a linear model applied to simulated data that encompasses a large range of data scenarios. Our results show that the new approach possesses the best type I error rates of all goodness-of-fit time series statistics

Tumbling, stretching and cross-stream migration of polymers in rectilinear shear flow from dissipative particle dynamics simulations

Solutions of flexible polymer chains with harmonic bonds undergoing rectilinear flow in slit pores are investigated via dissipative particle dynamics (DPD) simulations. We found that when DPD with low Schmidt number (Sc∼1) is used, the polymer chains tend to migrate across the streamlines towards the walls. However, a cross-stream migration towards the centerline is observed when DPD with relatively high values of Schmidt number (Sc∼10) is used. The effect of chain length and Weissenberg number, defined as Wi=Γτrel, where Γ and τrel are the shear rate and polymer longest relaxation time, respectively, are investigated. The polymer chains exhibit a large number of orientational and extensional fluctuations, with the distributions of both latitude and azimuthal angles exhibiting power-law decays in agreement with experiments, theory and previous simulations. The polymer chains exhibit tumbling kinetics characterized by an exponential distribution of tumbling times. The characteristic time scale is proportional to the longest relaxation time of the polymer chains at equilibrium. The power spectral density of the extension, while monotonically decaying for large chain length or large Weissenberg number, exhibits a shallow peak for short chains, implying that shear flow induces nearly repetitive tumbling of the polymer chains. The time scale corresponding to the peak of the extension power spectral density is also proportional to the longest chain relaxation time. © 2012 Elsevier B.V. All rights reserved

A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions.

BackgroundCardiovascular and other circulatory system diseases have been implicated in the severity of COVID-19 in adults. This study provides a super learner ensemble of models for predicting COVID-19 severity among these patients.MethodThe COVID-19 Dataset of the Cerner Real-World Data was used for this study. Data on adult patients (18 years or older) with cardiovascular diseases between 2017 and 2019 were retrieved and a total of 13 of these conditions were identified. Among these patients, 33,042 admitted with positive diagnoses for COVID-19 between March 2020 and June 2020 (from 59 hospitals) were identified and selected for this study. A total of 14 statistical and machine learning models were developed and combined into a more powerful super learning model for predicting COVID-19 severity on admission to the hospital.ResultLASSO regression, a full extreme gradient boosting model with tree depth of 2, and a full logistic regression model were the most predictive with cross-validated AUROCs of 0.7964, 0.7961, and 0.7958 respectively. The resulting super learner ensemble model had a cross validated AUROC of 0.8006 (range: 0.7814, 0.8163). The unbiased AUROC of the super learner model on an independent test set was 0.8057 (95% CI: 0.7954, 0.8159).ConclusionHighly predictive models can be built to predict COVID-19 severity of patients with cardiovascular and other circulatory conditions. Super learning ensembles will improve individual and classical ensemble models significantly