Applications of Machine Learning Methods in Health Outcomes Research: Heart Failure in Women

There is robust evidence that heart failure (HF) is associated with substantial mortality, morbidity, poor health-related quality of life, healthcare utilization, and economic burden. Previous research has revealed that there are sex differences in the epidemiology, etiology, and disease burden of HF. However, research on HF among women, especially postmenopausal women, is limited. To fill the knowledge gap, the three related aims of this dissertation were to: (1) identify knowledge gaps in HF research among women, especially postmenopausal women, using unsupervised machine learning methods and big data (i.e., articles published in PubMed); (2) identify emerging predictors (i.e., polypharmacy and some prescription medications) of incident HF among postmenopausal women using supervised machine learning methods; (3) identify leading predictors of HF-related emergency room use among postmenopausal women using supervised machine learning methods with data from a large commercial insurance claims database in the United States. This study utilized machine learning methods. In the first aim, non-negative matrix factorization algorithms were used to cluster HF articles based on the primary topic. Clusters were independently validated and labeled by three investigators familiar with HF research. The most understudied area among women was atrial fibrillation. Among postmenopausal women, the most understudied topic was stress-induced cardiomyopathy. For the second and third aims, a retrospective cohort design and Optum’s de-identified Clinformatics® Data Mart Database (Optum, Eden Prairie, MN), de-identified health insurance claims data, were used. In the second aim, multivariable logistic regression and three classification machine learning algorithms (cross-validated logistic regression (CVLR), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms) were used to identify predictors of incident HF among postmenopausal women. The associations of the leading predictors to incident HF were explored with an interpretable machine learning SHapley Additive exPlanations (SHAP) technique. The eight leading predictors of incident HF consistent across all models were: older age, arrhythmia, polypharmacy, Medicare, chronic obstructive pulmonary disease (COPD), coronary artery disease, hypertension, and chronic kidney disease. Some prescription medications such as sulfonylureas and antibiotics other than fluoroquinolones predicted incident HF in some machine learning algorithms. In the third aim, a random forest algorithm was used to identify predictors of HF-related emergency room use among postmenopausal women. Interpretable machine learning techniques were used to explain the association of leading predictors to HF-related emergency room use. Random forest algorithm had high predictive accuracy in the test dataset (Area Under the Curve: 94%, sensitivity: 93%, specificity: 77%, and accuracy: 0.81). We found that the number of HF-related emergency room visits at baseline, fragmented care, age, insurance type (Health Maintenance Organization), and coronary artery disease were the top five predictors of HF-related emergency room use among postmenopausal women. Partial dependence plots suggested positive associations of the top predictors with HF-related emergency room use. However, insurance type was found to be negatively associated with HF-related emergency room use. Findings from this dissertation suggest that machine learning algorithms can achieve comparable and better predictive accuracy compared to traditional statistical models

Discovering Jewish Studies Collections in Academic Libraries: A Practical Guide

The U.S. colleges and universities offering non-sectarian educational programs in Jewish Studies rely on the support of their academic libraries for research materials and library services. For college libraries which use Library of Congress Classification scheme, it is a common practice to integrate studies resources into their general library collections. Since Jewish Studies sources span a vast number of subjects within all major disciplines, shelving integration leads to the dispersion of all relevant sources and such dispersion in turn leads to a variety of problems for library professionals and library users. For collection development librarians the problems range from lack of information about collection\u27s size, strengths or weaknesses, and for library users interested in browsing the collection, dispersion of subjects creates a major roadblock. This practical guide aims at providing a solution to such problems. By identifying all relevant Library of Congress call numbers and the corresponding Library of Congress subject headings, the guide offers a simplified access to Jewish Studies sources in general library collections. It is arranged by four major discipline: Arts & Humanities, Social Sciences, Sciences, and General Works & Bibliographies. Within each discipline, specific LC call number ranges and corresponding subjects are listed. The subjects are further subdivided and precisely identified. The guide will assist collection development librarians, library liaisons, grants and fundraising professionals and especially the Jewish Studies faculty and students, in identifying and locating relevant sources

Study of the feasibility aspects of flight testing an aeroelastically tailored forward swept research wing on a BQM-34F drone vehicle

The aspects of flight testing an aeroelastically tailored forward swept research wing on a BQM-34F drone vehicle are examined. The geometry of a forward swept wing, which is incorporated into the BQM-34F to maintain satisfactory flight performance, stability, and control is defined. A preliminary design of the aeroelastically tailored forward swept wing is presented

DNA damage-induced transcription stress : a focus on RNA polymerase II

The integrity of DNA is continuously challenged by genotoxic insults from both endogenous and exogenous origin. Damaged DNA disrupts essential cellular functions such as replication and transcription, and can lead to mutagenesis, senescence, and apoptosis. If unrepaired, these DNA lesion may ultimately contribute to aging, or cause mutations that may give rise to cancer. To counteract these deleterious consequences, cells are equipped with an intricate network of highly regulated processes that orchestrate the recognition and dedicated repair of DNA lesions and the activation of DNA damage-induced cell signaling pathways. In this thesis we investigated the cellular consequences of DNA-damage induced transcription stress, with a special focus on the regulation of RNA polymerase II function after UV irradiation