Browsing by Author "Chen, Su"

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    Derivation and performance of an end-of-life practice score aimed at interpreting worldwide treatment-limiting decisions in the critically ill
    (Springer Nature, 2022) Mentzelopoulos, Spyros D.; Chen, Su; Nates, Joseph L.; Kruser, Jacqueline M.; Hartog, Christiane; Michalsen, Andrej; Efstathiou, Nikolaos; Joynt, Gavin M.; Lobo, Suzana; Avidan, Alexander; Sprung, Charles L.; Ely, Wesley; Kompanje, Erwin J.O.; Mer, Mervyn; Feldman, Charles; Metaxa, Victoria; Shinall, Myrick C.; Myburgh, John; Vrettou, Charikleia S.; End-of-life Practice Score Study Group; Rice D2K Lab
    Limitations of life-sustaining interventions in intensive care units (ICUs) exhibit substantial changes over time, and large, contemporary variation across world regions. We sought to determine whether a weighted end-of-life practice score can explain a large, contemporary, worldwide variation in limitation decisions.
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    Fondren Library Data Repository for Data Science Education and Experiential Learning
    (Rice University, 2023-06-15) Xiong, Anna; Chen, Su; Barber, Catherine R.; Sun, Nik; Qiu, Alison; Li, Tina
    This project piloted a process for creating a repository of interesting, real-world government datasets that are easy to access, beginner-friendly, and suitable for educational use, particularly in data science. The project resulted in three sub-projects, each of which uses one or more open government datasets to demonstrate the data science pipeline. The first sub-project (1_mental_health_project) used the U.S. Census Bureau's Household Pulse survey to explore correlates of mental health during the COVID-19 pandemic. The second sub-project (2_education_demographics_project) used the National Center for Education Statistics' National Household Education Survey and Common Core of Data along with the Texas Education Agency's graduation data to explore relationships among educational outcomes, student and family demographic variables, and county demographic diversity within the 12th grader population. The third sub-project (3_economics_employment_project) used the U.S. Census Bureau's Current Population Survey and a wide range of financial data (COVID-related spending, Medicaid spending, GDP, and minimum wage) to explore the relationship beteen government fiscal relief measures and employment during recessions. The three sub-project folders include clean datasets, code for cleaning and analyzing the data, and interpretation of the results. These materials are suitable for a range of learners within data science, including both novices and those with advanced statistical skills.