A Systematic Framework for Radio Frequency Identification (RFID) Hazard Mitigation in the Blood Transfusion Supply Chain from Donation to Distribution

The RFID Consortium is developing what will be the first FDA-approved use of radio frequency identification (RFID) technology to identify, track, manage, and monitor blood throughout the entire blood transfusion supply chain. The iTraceTM is an innovative technological system designed to optimize the procedures currently employed when tracing blood from the donor to the recipient. With all novel technologies it is essential to consider not only the advantages, but also the potential harms that may come about from using the system. The deployment of the iTraceTM consists of two phases: 1) Phase One - application of the iTraceTM from the donor to blood center distribution, and 2) Phase Two - application of the iTraceTM from blood center distribution to transfusion. This dissertation seeks to identify the possible hazards that may occur when utilizing the iTraceTM during Phase One, and to assess the mitigation and correction processes to combat these hazards. A thorough examination of verification and validation tests, as well as of the system design, requirements, and standard operating procedures was performed to qualify and quantify each hazard into specific categories of severity and likelihood. A traceability matrix was also established to link each hazard with its associated tests and/or features. Furthermore, a series of analyses were conducted to determine whether the benefits of implementing the iTraceTM outweighed the risks and whether the mitigation and correction strategies of the hazards were effective. Ultimately, this dissertation serves as a usable, generalizable framework for the management of RFID-related hazards in the blood transfusion supply chain from donor to blood center distribution

Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas

Your Right to Look Like an Ugly Criminal: Resolving the Circuit Split over Mug Shots and the Freedom of Information Act

Mug shots occupy a seemingly indelible place in American popular culture. Embarrassing booking photos of celebrities like Lindsay Lohan,\u27 Mel Gibson, and Robert Downey, Jr. are plastered on televisions and tabloids across the country. Local newspapers feature the most recent mug shots from the nearby jail, and mug shot websites are increasingly common. Perhaps our fascination with these images stems from the same impulse driving the popularity of reality television: seeing real people in bad situations makes us feel better about our own lives

The Application of Text Mining and Data Visualization Techniques to Textual Corpus Exploration

Unstructured data in the digital universe is growing rapidly and shows no evidence of slowing anytime soon. With the acceleration of growth in digital data being generated and stored on the World Wide Web, the prospect of information overload is much more prevalent now than it has been in the past. As a preemptive analytic measure, organizations across many industries have begun implementing text mining techniques to analyze such large sources of unstructured data. Utilizing various text mining techniques such as n -gram analysis, document and term frequency analysis, correlation analysis, and topic modeling methodologies, this research seeks to develop a tool to allow analysts to maneuver effectively and efficiently through large corpuses of potentially unknown textual data. Additionally, this research explores two notional data exploration scenarios through a large corpus of text data, each exhibiting unique navigation methods analysts may elect to take. Research concludes with the validation of inferential results obtained through each corpus’s exploration scenario

Integrative disease classification based on cross-platform microarray data

<p>Abstract</p> <p>Background</p> <p>Disease classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based disease classification.</p> <p>Results</p> <p>In this study, we tested the feasibility of disease classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank ratios across datasets. In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and automated construction of disease classes. We design a new classification approach named ManiSVM, which integrates Manifold data transformation with SVM learning to exploit the data properties. Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many disease classes achieving the accuracy higher than 80%.</p> <p>Conclusion</p> <p>Our results not only demonstrated the feasibility of the integrated disease classification approach, but also showed that the classification accuracy increases with the number of homogenous training datasets. Thus, the power of the integrative approach will increase with the continuous accumulation of microarray data in public repositories. Our study shows that automated disease diagnosis can be an important and promising application of the enormous amount of costly to generate, yet freely available, public microarray data.</p

Tsetlin Machine for Fake News Detection: Enhancing Accuracy and Reliability

This thesis aims to improve the accuracy of fake news detection by using Tsetlin Machines (TM). TMs are well suited for noisy and complex relations within the provided data, which on initial analysis, overlaps nicely with characteristics found in fake news. We provide a performant and deterministic preprocessor, which is responsible for tokenizing, lemmanzing, and encoding to a representation that the TM understands. We compare our approach with TMs against Neural Networks (NN) models over a variety of well-known datasets within the fake news domain. Our findings show from comparable results to significant improvements over state of the art. Additionally, we show how TMs allow for interpretable propositional logic rules. For datasets with 2 classifications, we further convey these rules during inference by applying a color between red and green, which shows the intensity and what direction each word pulls the classification towards

Essays in political text: new actors, new data, new challenges

The essays in this thesis explore diverse manifestations and different aspects of political text. The two main contributions on the methodological side are bringing forward novel data on political actors who were overlooked by the existing literature and application of new approaches in text analysis to address substantive questions about them. On the theoretical side this thesis contributes to the literatures on lobbying, government transparency, post-conflict studies and gender in politics. In the first paper on interest groups in the UK I argue that contrary to much of the theoretical and empirical literature mechanisms of attaining access to government in pluralist systems critically depend on the presence of limits on campaign spending. When such limits exist, political candidates invest few resources in fund-raising and, thus, most organizations make only very few if any political donations. I collect and analyse transparency data on government department meetings and show that economic importance is one of the mechanisms that can explain variation in the level of access attained by different groups. Furthermore, I show that Brexit had a diminishing effect on this relationship between economic importance and the level of access. I also study the reported purpose of meetings and, using dynamic topic models, show the temporary shifts in policy agenda during this period. The second paper argues that civil society in post-conflict settings is capable of high-quality deliberation and, while differing in their focus, both male and female can deliver arguments pertaining to the interests of broader societal groups. Using the transcripts of civil society public consultation meetings across former Yugoslavia I show that the lack of gender-sensitive transitional justice instruments could stem not from the lack of women’s 3 physical or verbal participation, but from the dynamic of speech enclaves and topical focus on different aspects of transitional justice process between genders. And, finally, the third paper maps the challenges that lie ahead with the proliferation of research that relies on multiple datasets. In a simulation study I show that, when the linking information is limited to text, the noise can potential occur at different levels and is often hard to anticipate in practice. Thus, the choice of record linkage requires balancing between these different scenarios. Taken together, the papers in this thesis advance the field of “text as data” and contribute to our understanding of multiple political phenomena