Browsing by Subject "data science"

Now showing 1 - 6 of 6
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    AIMS Philanthropy Project: Studying AI, Machine Learning & Data Science Technology for Good
    (Indiana University Lilly Family School of Philanthropy and Indiana University School of Informatics and Computing, IUPUI, Indianapolis, IN., 2021-02-07) Herzog, Patricia Snell; Naik, Harshal R.; Khan, Haseeb A.
    This project investigates philanthropic activities related to Artificial Intelligence, Machine Learning, and Data Science technology (AIMS). Advances in AIMS technology are impacting the field of philanthropy in substantial ways. This report focuses on methods employed in analyzing and visualizing five data sources: Open Philanthropy grants database, Rockefeller Foundation grants database, Chronicle of Philanthropy article database, GuideStar Nonprofit Database, and Google AI for Social Good grant awardees. The goal was to develop an accessible website platform that engaged human-centered UX user experience design techniques to present information about AIMS Philanthropy (https://www.aims-phil.org/). Each dataset was analyzed for a set of general questions that could be answered visually. The visuals aim to provide answers to these two primary questions: (1) How much funding was invested in AIMS? and (2) What focus areas, applications, discovery, or other purposes was AIMS-funded directed toward? Cumulatively, this project identified 325 unique organizations with a total of $2.6 billion in funding for AIMS philanthropy.
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    Community Studies of Antisemitism in Schools (CSAIS) Community Typology Explorer
    (2021) Price, Jeremy F.; Wilson, Jeffrey S.; Schall, Carly E.; Snorten, Clifton L.; Hasan, Mohammad A.; Luo, Xiao; Jahin, S. M. Abrar
    This is a companion document to the CSAIS (Community Studies of Antisemitism In Schools) Community Typology Explorer which can be found at https://jeremyfprice.github.io/csais-dashboard/. Details about specific incidents, communities, and community types can be found at the CSAIS Community Typology Explorer. This project utilizes data from the ADL H.E.A.T. Map between 2016 and 2019 to identify incidents of antisemitism that specifically took place in schools. These incidents in schools are influenced by demographic, historical, social, and political factors. This project brings this data together to construct a community typology at the national level. This typology will provide insight into the ways that school-based incidents of hate are enacted and reported in context. Developing a community typology will allow providers to better target specific demographic, historical, and political attributes of the communities in which these incidents occur through curriculum and learning experiences.
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    Crafting an Innovative Model for Developing an Online Data Curriculum
    (2021-09) Murillo, Angela P.
    This poster presents the preliminary findings and observations of developing the undergraduate Applied Data and Information Science (ADIS) Bachelor of Science program. The ADIS program incorporates competencies and skillsets from Library and Information Science and Data Science and is an interdisciplinary collaboration between an LIS Department and a Human-Centered Computing department. The LIS courses in this program are online asynchronous courses. This poster presents the preliminary findings and observations regarding program development, curriculum development, course development, and online course delivery to undergraduates. This poster will present the LIS and data science models and frameworks that were utilized to develop the program learning outcomes from the program development perspective. This poster will discuss the specific LIS and data science competencies embedded into the curriculum from the curriculum development perspective. This poster will present examples of how specific data skill sets and competencies are incorporated into the course from the course development perspective. Lastly, this course will discuss best practices for delivering hands-on data-related curriculum to undergraduates in an online environment from an online course delivery perspective. Although this poster focuses on undergraduate program development, similar models can be used for the creation of masters-level data-related program development, as well as the lessons learned from the delivery of online asynchronous hands-on data-related courses. Strategic partnerships, data-related curriculum, and online course delivery are highly relevant for all levels of current and future LIS education and program development.
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    Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning
    (Elsevier, 2019-12) Perron, Brian E.; Victor, Bryan G.; Bushman, Gregory; Moore, Andrew; Ryan, Joseph P.; Lu, Alex Jiahong; Piellusch, Emily K.; School of Social Work
    Background State child welfare agencies collect, store, and manage vast amounts of data. However, they often do not have the right data, or the data is problematic or difficult to inform strategies to improve services and system processes. Considerable resources are required to read and code these text data. Data science and text mining offer potentially efficient and cost-effective strategies for maximizing the value of these data. Objective The current study tests the feasibility of using text mining for extracting information from unstructured text to better understand substance-related problems among families investigated for abuse or neglect. Method A state child welfare agency provided written summaries from investigations of child abuse and neglect. Expert human reviewers coded 2956 investigation summaries based on whether the caseworker observed a substance-related problem. These coded documents were used to develop, train, and validate computer models that could perform the coding on an automated basis. Results A set of computer models achieved greater than 90% accuracy when judged against expert human reviewers. Fleiss kappa estimates among computer models and expert human reviewers exceeded .80, indicating that expert human reviewer ratings are exchangeable with the computer models. Conclusion These results provide compelling evidence that text mining procedures can be a cost-effective and efficient solution for extracting meaningful insights from unstructured text data. Additional research is necessary to understand how to extract the actionable insights from these under-utilized stores of data in child welfare.
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    Integrating Data Science into T32 Training Programs at IUPUI
    (2019-06-30) Dixon, Brian E.; Stumpff, Julia C.; Kasthurirathne, Suranga N.; Lourens, Spencer; Janga, Sarath; Liu, Yunlong; Huang, Kun
    Data science is critically important to the biomedical research enterprise. Many research efforts currently and in the future will employ advanced computational techniques to analyze extremely large datasets in order to discover insights relevant to human health. Therefore the next generation of biomedical scientists requires knowledge of and proficiency in data science. With support from the U.S. National Library of Medicine, a team of faculty from Indiana University-Purdue University Indianapolis (IUPUI) facilitated curricula enhancement for National Institutes of Health (NIH) T32 research training programs with respect to data science. In collaboration with the existing NIH T32 Program Directors at IUPUI and the IU School of Medicine, the interdisciplinary team of faculty drawn from multiple schools and departments examined the existing landscape of data science offerings on campus in parallel with an assessment of the competencies that future biomedical and clinician scientists will require to be comfortable using data science methods to advance their research. The IUPUI campus possesses a rich tapestry of data science education programs across multiple schools and departments. Furthermore, the campus is home to more than a dozen world-class T32 programs funded by the NIH to train biomedical and clinician scientists. However, existing training programs do not currently emphasize data science or provide specific curriculum designed to ensure T32 graduates possess basic competencies in data science. To position the campus for the future, robust T32 programs need to connect with the rapidly growing data science programs. This report summarizes the rationale for the importance of connection and the competencies that future biomedical and clinical scientists will require to be successful. The report further describes the curriculum mapping efforts to link competencies with available degree programs, courses and workshops on campus. The report further recommends next steps for campus leadership, including but not limited to T32 Program Directors, the Office of the Vice Chancellor for Research, the Executive Associate Dean for Research Affairs at the IU School of Medicine, and the President and CEO of the Regenstrief Institute. Together we can strengthen the IUPUI campus and help ensure its T32 graduates are successful in their research careers.
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    Root Canal Treatment Survival Analysis in National Dental PBRN Practices
    (Sage, 2022-10) Thyvalikakath, T.; LaPradd, M.; Siddiqui, Z.; Duncan, W. D.; Eckert, G.; Medam, J. K.; Rindal, D. B.; Jurkovich, M.; Gilbert, G. H.; National Dental PBRN Collaborative Group; Dental Public Health and Dental Informatics, School of Dentistry
    Few studies have examined the longevity of endodontically treated teeth in nonacademic clinical settings where most of the population receives its care. This study aimed to quantify the longevity of teeth treated endodontically in general dentistry practices and test the hypothesis that longevity significantly differed by the patient’s age, gender, dental insurance, geographic region, and placement of a crown and/or other restoration soon after root canal treatment (RCT). This retrospective study used deidentified data of patients who underwent RCT of permanent teeth through October 2015 in 99 general dentistry practices in the National Dental Practice-Based Research Network (Network). The data set included 46,702 patients and 71,283 RCT permanent teeth. The Kaplan–Meier (product limit) estimator was performed to estimate survival rate after the first RCT performed on a specific tooth. The Cox proportional hazards model was done to account for patient- and tooth-specific covariates. The overall median survival time was 11.1 y; 26% of RCT teeth survived beyond 20 y. Tooth type, presence of dental insurance any time during dental care, placement of crown and/or receiving a filling soon after RCT, and Network region were significant predictors of survival time (P < 0.0001). Gender and age were not statistically significant predictors in univariable analysis, but in multivariable analyses, gender was significant after accounting for other variables. This study of Network practices geographically distributed across the United States observed shorter longevity of endodontically treated permanent teeth than in previous community-based studies. Also, having a crown placed following an RCT was associated with 5.3 y longer median survival time. Teeth that received a filling soon after the RCT before the crown was placed had a median survival time of 20.1 y compared to RCT teeth with only a crown (11.4 y), only a filling (11.2 y), or no filling and no crown (6.5 y).