Browsing by Subject "big data"
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Item Big Data Limitations: The Sky is the Limit for Overcoming Big Data Barriers(2017-10) Vaughn, PorciaOBJECTIVE: As a means to embed in interdisciplinary research, the Biosciences Librarian found an opportunity to be involved in a research initiative called a Pop-Up Institute (PUI), Understanding Individual Population Variation in Biology, Medicine, and Society. This was a one-month interdisciplinary research team that would work towards 1) identify the most promising questions about individual and population variation, 2) establish a unique and comprehensive research plan and 3) develop solutions to shared problems that limit progress (e.g. Big Data analysis and data sharing). METHODS: Informal notes and a formal survey were taken to identify the benefits, needs, and services of a librarian being embedded in interdisciplinary research. RESULTS: Feedback and perspectives from the PUI research participants along with survey response showed that the involvement of the library was “extremely useful”• (85%). Additionally, 78% of survey respondents are “extremely likely”• to contact the library or librarian for future research, teaching, or projects. CONCLUSION: The Biosciences Librarian successfully embedded and is continuing to provide services that will impact researchers and clinicians who are facing big data challenges. The PUI revealed five needs for our campus to overcome big data limitations: 1) identify a person to connect and establish research partnerships across the institution, 2) document and curate the resources and infrastructure already in place, 3) identify data sources on and off our campus that will enhance data modeling, 4) identify systematic data collection and analysis methods across departments, and 5) identify unique funding opportunities.Item China’s Biomedical Data Hacking Threat: Applying Big Data Isn’t as Easy as It Seems (Summer 2022)(Texas National Security Review, 2022) Vogel, Kathleen M.; Ouagrham-Gormley, Sonia BenConcerns have developed in recent years about the acquisition of U.S. biomedical information by Chinese individuals and the Chinese government and how this creates security and economic threats to the United States. And yet, China’s illicit acquisition of data is only one aspect of what is required to produce an enhanced science and technology capability that would pose a security threat. Current assessments fail to account for the heterogeneity of big data and the challenges that any actor (state or nonstate) faces in making sense of this data and using it. In this context, current law enforcement and policies that focus on the Chinese acquisition of biomedical big data should expand to other important aspects of China’s science and technology capabilities, including the country’s ability to interpret, integrate, and use the acquired data for its economic or military benefit. This article provides new socio-technical frameworks that can be used to provide greater insights into Chinese threats involving biomedical big data.Item The Emergence of Big Data Policing(University of Texas at Austin Population Research Center, 2017-08) Brayne, SarahItem Predictive Modeling on Cardiovascular Health(2023-12) Dinh,JasWith expenses escalating in American healthcare, leveraging data analytics can help cut costs and improve patient satisfaction. Employing machine learning for predictive modeling can pinpoint high-risk patients, enabling proactive care instead of reactive. In my thesis, I replicated healthcare data analytics studies to showcase the potency of machine learning. I compared methods, algorithms, and models while highlighting ethical issues in big data analytics for healthcare. In the study analysis, Dristas & Trigka (2022) exhibited the most replicable and comprehensive results. Tazin et al. (2021) erred by including synthetic samples in the testing set during the SMOTE process while Dev et al. (2022) used an under-sampling technique, diminishing an already small dataset and risking accuracy issues. Despite the immense potential of machine learning in healthcare, my results revealed execution flaws that highlight the importance of additional research to validate big data analytics in healthcare. Replicating studies is crucial for making well-informed decisions based on reliable evidence. A collaborative effort between data scientists, healthcare professionals, and policymakers is essential to safeguard patient privacy and ensure responsible technology use in healthcare.