USC Big Data Health Science Center

BDHSC 2020 Pilot Project Showcase
2021-04-28 11:00 - 12:00 UTC
Three recipients will be presenting their research questions, study design, protocol and progress to date. Projects include “Leveraging the Power of Big Data for Predicting Future STDs among PLWH: A Pilot Study” by Dr. Bankole Olatosi, “Using EHR and Community Data to Predict Medication-Related Post-Discharge Acute Care Utilization” by Dr. Ronda Hughes and “An Investigation of Racial and Geospatial Disparities in the Utilization, Adherence, and Economic Cost to Targeted Therapies for Breast Cancer” by Dr. Swann Arp Adams.
Register here, https://us02web.zoom.us/webinar/register/WN_AwyJTeXIQ_y8fDTC_8Hwjg

Seminar Series: Knowledge-Aware Suicide Risk Prediction
2021-04-21 11:00 - 12:30
Register for this free virtual event at, https://us02web.zoom.us/webinar/register/WN_g89PZa94TIGx3IYtRUoyJQ

2021 Pilot Project Proposals Due at 5:00pm
2021-04-15
University of South Carolina Big Data Health Science Center (BDHSC) is seeking proposals for the conduct and uptake of pilot research projects focusing on the Big Data analytics in addressing critical issues related to health behavior, healthcare, and population health.
Timeline:
Announcement Release Data: January 11, 2021
Application Receipt Date: April 15, 2021 by 5 p.m.
Notification of Outcome: All applicants will receive notification by June 30, 2021
Earliest Project Start Date: August 16, 2021

Download RFP Here

Seminar Series: Twitter-derived measures of sentiment towards minorities and associations with adverse birth outcomes and cardiovascular disease by Dr. Quynh Nguyen
2021-03-24 11:00 - 12:30 | Zoom Webinar
Seminar Description:Interpersonal and structural racial bias are leading explanations for the continuing racial disparities in birth outcomes but research to confirm the role of racism has been hampered by challenges in both measuring racial bias and evaluating its impact.
We use Twitter data to characterize area-level racial hostility and examine the associations with adverse birth and cardiovascular outcomes. In this webinar, we cover Twitter data collection and processing, sentiment analysis, and use of machine learning to classify tweets for racist content.
Use of nontraditional data sources like Twitter has the potential to lead to greater tracking of area-level racial bias and to provide essential information needed to develop interventions to reduce the impact of racial bias on health.
About the Speaker: Dr. Quynh Nguyen, PhD, MSPH, is an assistant Professor of Epidemiology and Biostatistics at the University of Maryland School of Public Health. Her current research program focused on creating and validating neighborhood indicators constructed form nontraditional Big Data sources such as social media data and Google Street view images.
Register Here:https://us02web.zoom.us/webinar/register/WN_8wJJ_X0IQyqHfzpXRO7PRg

Tales from the Botanical Crypt: How Digitized Herbarium Collections Can Provide New Avenues for Salient Research
2021-03-18 11:00 - 12:30
Seminar Description:Join Dr. Herrick Brown, Curator of the A.C. Moore Herbarium for this virtual seminar.
Vast amounts of biodiversity data lie safely tucked behind closed doors in natural history collections across the globe. Until recently these collections remained largely inaccessible to the general public. Even specialized research involving the collections was difficult and progressed at a glacial pace.
These challenges were addressed by over 100 herbaria across the Southeastern United States in an NSF-funded, collaborative project to digitize an estimated 4.7 million specimens. Designed to facilitate research based on these primary reference materials, the project has exceeded its goal and currently offers over 5.1 million specimen records and counting.
The breadth of research topics based on these data has expanded from traditional taxonomic investigations to AI; and emerging studies involving rare species conservation, biogeography, and climate change continue to make use of this growing data set. Herbarium specimen data may also have some relevance involving public health. For instance, documented phenological shifts may help predict the beginning and duration of seasonal allergens associated with pollen dispersal. Species distribution models using forecast climate conditions may help predict emerging areas where allergenic species were not previously known to occur.
Where: Via Zoom
When: Thursday, March 18th from 11:00-12:30pm
Register in advance for this webinar: https://us02web.zoom.us/webinar/register/WN_TvG6RTahTsOcB3n-FcJodQ

Technology Hub Event- UofSC ITT Lab Innovation Think Tank Certification Program
2021-03-08 - 2021-03-10 | Virtual
The University of South Carolina (UofSC) Innovation Think Tank (ITT) Lab is organizing a virtual Innovation Think Tank Certification Program (ITT CP)which will be held on March 8-10, 2021 with the purpose to develop innovative solutions for real world problems in healthcare and related sectors. This event is a part of BDHSC Technology Hub’s activities. ITT CP is an experiential learning program based on the Innovation Think Tank Approach and structure developed by Sultan Haider, Founder and Head at Siemens Healthineers Innovation Think Tank; Program Director at the UofSC ITT Lab, with unique experience of executing innovation infrastructures, ITT programs and labs at Siemens Healthineers and a number of prestigious institutions worldwide adaptable to changing business and research environments. At the program, the participants will learn co-implementation methodologies and approaches working on real-life challenges of global healthcare systems. It is open to all university undergraduate and graduate students enrolled at any university in the U.S. and from any program of study or concentration. Deadline for Application is March 1st 2021.

Canceled: Seminar by Dr. Sayan Mukherjee
2021-02-24 11:00 - 12:30
This event has been canceled.

Big Data Health Science Conference 2021
2021-02-05 09:00 - 2021-02-06 17:00 | Virtual
It is with great pleasure that we invite you to the 2021 Big Data Health Science Conference on February 5-6, 2021. The theme for the 2021 virtual conference is “Unlocking the Power of Big Data in Health – Bringing Innovation into Improved Care and Prevention”. The conference is full of can’t-miss talks and sessions. We are pleased to have such a diverse group of distinguished keynote speakers from various areas of the health sciences, government, and academia! Registration details can be found on our conference website https://www.sc-bdhs-conference.org.

Big Data Health Science Virtual Student Case Competition 2021
2021-01-29 12:00 - 2021-01-31 17:00 | Virtual
The 2nd Annual Big Data Health Science Case Competition will be held virtually between January 29– 31, 2021 before the University of South Carolina’s National Big Data Health Science Conference, scheduled for February 5– 6, 2021. Last year’s winner was Duke University, so it is time for others to compete to dethrone them. The Big Data Case Competition is intended to provide enthusiastic teams of graduate and senior undergraduate students with the opportunity to apply their knowledge to the analysis of big datasets in healthcare.

This year’s challenge will focus on solving the inappropriate use of healthcare using a data analytics approach. Each participating team will analyze the case and datasets. During this period, the teams must identify the most prominent issues and develop a strategy to address key issues to help healthcare organizations achieve their goals of reducing inappropriate care utilization. Team members will work together to present their methods, analyses, and results at the Big Data Health Science Center Case Competition virtually. A panel of industry and academic experts will judge the presentations based on each team’s use of full analytics tools/processes, from framing the problem to data use, model building, innovation and communicating the solutions to decision-makers. Register here https://www.sc-bdhs-conference.org/2021-case-competition/

Seminar: BDHSC Pilot Project Showcase
2021-01-27 11:00 - 12:30
Join us for a virtual seminar showcasing 3 recipients of the 2020 BDHSC Pilot Project Awards and get details about the 2021 Request for Proposals.
Rehister here : https://us02web.zoom.us/webinar/register/3016101376482/WN_K_0DaS3YTfqWjF0Kfl8zhg

Virtual Seminar: Using Social Media and Electronic Health Records Data to Address Behavioral Health Research Questions
2020-12-16 11:00 - 12:30 | Zoom Webinar
Register: Interested in attending? Register via Zoom here https://bit.ly/2V44bO5

Description: In this seminar, Dr. Conway will present recent work on investigating behavioral health research questions using social media text (Twitter, Reddit) and electronic health record data, concentrating on broad areas of depression and substance abuse. He will also focus on the ethical issues that arise when utilizing social media for behavioral health research.
About the Author: Since earning his PhD from the University of Sheffield’s Department of Computer Science in 2007, Dr. Conway’s research has focused on using informatics methods – particularly natural language processing – to address research questions in population health. His recent work, funded by the NIH/National Institute on Drug Abuse and NIH/National Library of Medicine, utilizes social media and clinical notes to investigate both how social media users discuss their substance use (particularly focusing on cannabis, combustible tobacco, and electronic nicotine delivery systems) and how clinicians document substance use in the electronic health record.
Register: Interested in attending? Register via Zoom here https://bit.ly/2V44bO5

Health GIS Scholars Program Applications Due
2020-11-20 17:00
Health GIS Scholars Program
The Big Data Health Science Center’s Geospatial Core is pleased to announce the inaugural Health Geographic Information Science (GIS) Scholars Program. This program is being launched to recognize and support two outstanding undergraduate or graduate students who have demonstrated interest, potential, and/or experience in GIS and health research. GIS and health research is broadly defined, and includes, but is not limited to, using GIS to evaluate the social determinants of health, health behaviors, health outcomes, access to health care and social services, utilization of health services, environmental exposures, built environment, and other health-related factors through mapping and spatial analysis. The goals of this program are to:

Enhance students’ research and professional development in the area of GIS and health research
Cultivate students’ interest in GIS and spatial applications to health research
Build the technical and writing skills of students to pursue scholarly publications and reports
Develop scholars in health GIS who go on to make important contributions to the academic, public health, health care, and non-profit sectors

Two student scholars will be awarded $2,500 each, which can be used toward professional development activities and expenses including resources and supplies for data collection and analysis, travel and registration at national or international conferences where research is presented on this topic, for professional workshops, or for other continuing education/training opportunities of importance to GIS and health research. Up to $2,000 can be requested for stipend and fringe for the applicant. These funds are not expected to take the place of resources available through existing graduate research or teaching assistantships, but are rather intended as supplements.
Scholars will be expected to engage in research and professional development activities with the Big Data Health Science Center during the award period as well as present preliminary findings at one or more research events/conferences (e.g., Big Data Health Science Center Seminar Series, Discover UofSC, and/or the James Clyburn Health Disparities Lecture) and will be strongly encouraged to submit the full findings from their study within a year. The award funds must be used within 9 months of receipt and all expenses must be pre-approved by the Director or Co-Director of the BDHSC Geospatial Core.
Student finalists will be selected based on a review of the following materials:

Curriculum Vitae.
Research Statement (no more than 1500 words not including references) that includes the following: 1) outlines the applicant’s experience and interest in GIS and spatial analysis for health-related research and/or practice, and 2) describes 1-2 key research objectives, as well as brief background, proposed data sources, methods, and deliverables. The GIS Scholar’s project may be topically and methodologically aligned with the Scholar’s mentor’s research program, but the proposed objectives must be developed by the student and distinct from the mentor’s work.
Proposed budget and budget justification for how the award would support the applicant’s research plan
A signed letter of support from a UofSC system faculty member in support of the student’s application and indicating their agreement to serve as the student’s mentor/supervisor for the proposed project and noting the distinction between the student’s proposed project and the mentor’s ongoing research

Eligibility Requirements:

Enrolled at the masters or doctoral level at the University of South Carolina (Columbia or regional campuses) with an anticipated graduation date of December 2021 or later OR be an undergraduate student in their junior year who has completed at least one college course in GIS (e.g., GEOG 341 or GEOG 363)
Be in good academic standing (average 3.0 GPA or higher)

Application Deadline:
November 20, 2020 by 5 PM EST
Please direct any questions and submit your application to:
Whitney Zahnd, PhD
Research Assistant Professor, Rural & Minority Health Research Center
Co-Director, Big Data Health Science Center Geospatial Core
zahnd@mailbox.sc.edu

Multivariate Spatial Data Analysis in GeoDa Workshop
2020-10-29 10:00 - 11:30
Workshop Description: Spatial data analysis and multivariate applications are increasingly needed to address complex, geographically correlated phenomena. This workshop will cover spatial data handling in GeoDa, exploratory spatial data basics of areal data (eg. choropleth maps, parallel coordinate plots, etc), and some advanced multivariate analysis. Participants will conduct a principal component analysis and k-means cluster analysis with spatial data, mapping outcomes at each stage for additional exploration and understanding of regional dynamics. We will use relevant social determinants of health variables for the continental U.S. using 2018 5-year average Census data, and calculate US-wide multidimensional indices and neighborhood typologies at the census tract scale. Participants should download GeoDa prior to the workshop (free and available for Mac, Windows, or Linux): https://geodacenter.github.io/download.html. Additional data and tutorials will be made available to participants prior to the workshop.

Register: This workshop is free and wil take place over Zoom. Register using the link below, https://us02web.zoom.us/webinar/register/WN_4XiFRwMkTbS7uwwq_P0wZw

About the Speaker:Marynia Kolak, MS, MFA, PhD, ​is a health geographer and data scientist using open science tools and an exploratory data analytic approach to investigate issues of equity across space and time. Her research centers on how “place” impacts health outcomes in different ways, for different people, from opioid risk environments to chronic disease clusters. She focuses on quantifying and distilling the structural determinants of health across different environments, tying socio-ecological models of public health with geocomputational methods and quasi-experimental policy evaluation techniques.
She received the 2017 Concordium Innovation Award at AcademyHealth for her open-source visualization of Chicago neighborhood health indicators, and “Highest Impact” award in the Prevention Category at the American College of Cardiology 2019 conference for her work in connecting chronic disease rates with social determinants of health. She serves as the lead PI for the US Covid Atlas Project in part funded by the Robert Wood Johnson Foundation, PI for two air quality “smart city” projects with the Chicago Department of Public Health, Co-I for an NIH project investigating the opioid epidemic in rural Illinois, and is the spatial analytic lead at the Methodology and Advanced Analytics Resource Center for the Justice Community Opioid Innovation Network (NIH HEAL Initiative).
Marynia is the Assistant Director of Health Informatics and Senior Lecturer in GIScience at the Center for Spatial Data Science, University of Chicago. She additionally serves as a Health and Medical Specialty Group (AAG) board member and is chair of the Chicago Public Health GIS Network. She received her Ph.D in Geography at Arizona State University, M.F.A in Writing from Roosevelt University, M.S. in GIS from John Hopkins University, and B.S. in Geology from the University of Illinois at Urbana-Champaign.

Quantifying Neighborhood-Level Social Determinants of Health and Risk Landscapes Seminar
2020-10-28 11:00 - 12:30 | Zoom Webinar
About the Event:Associations between social and neighborhood characteristics and health outcomes are well known but remain poorly understood owing to complex, multidimensional factors that vary across geographic space. Growing interest in quantifying social determinants of health (SDOH) at a small-area resolution must account for such complexity. In a recent cross-sectional study, a Kolak-led team developed multidimensional SDOH indices and a regional typology of the continental U.S. at a small-area level using dimension reduction and clustering machine learning techniques, spatializing results at each stage. Four SDOH indices accounted for 71% of the variance across all census tracts. The neighborhood typology of extreme poverty, which is of greatest concern to health care practitioners and policy advocates, comprised only 9.6% of all tracts characterized small areas of known public health crises. An association was observed between all SDOH indices and age-adjusted premature mortality rates in Chicago, even after accounting for violent crime and spatial structures.
The modeling of SDOH as multivariate, geographic phenomena may better capture the complexity and spatial heterogeneity underlying SDOH and associated disparities in health outcomes. Extensions of this work may also characterize and define risk landscapes in complex environments, from the opioid epidemic to COVID-19 pandemic. For example, the US Covid Atlas Project integrates regional contextual factors within a dynamic hotspot surveillance application. During a time of increased attention to SDOH, a spatially explicit approach may provide actionable information for key stakeholders with respect to the focus of interventions — and better understand what constitutes, drives, and sustains resilient communities.

Details: This event is free and will take place via Zoom. You may use the following link to register, https://us02web.zoom.us/webinar/register/WN_D7977Zh9RPaRM8UO5E48LA

About the Speaker:Marynia Kolak, MS, MFA, PhD, ​is a health geographer and data scientist using open science tools and an exploratory data analytic approach to investigate issues of equity across space and time. Her research centers on how “place” impacts health outcomes in different ways, for different people, from opioid risk environments to chronic disease clusters. She focuses on quantifying and distilling the structural determinants of health across different environments, tying socio-ecological models of public health with geocomputational methods and quasi-experimental policy evaluation techniques.
She received the 2017 Concordium Innovation Award at AcademyHealth for her open-source visualization of Chicago neighborhood health indicators, and “Highest Impact” award in the Prevention Category at the American College of Cardiology 2019 conference for her work in connecting chronic disease rates with social determinants of health. She serves as the lead PI for the US Covid Atlas Project in part funded by the Robert Wood Johnson Foundation, PI for two air quality “smart city” projects with the Chicago Department of Public Health, Co-I for an NIH project investigating the opioid epidemic in rural Illinois, and is the spatial analytic lead at the Methodology and Advanced Analytics Resource Center for the Justice Community Opioid Innovation Network (NIH HEAL Initiative).
Marynia is the Assistant Director of Health Informatics and Senior Lecturer in GIScience at the Center for Spatial Data Science, University of Chicago. She additionally serves as a Health and Medical Specialty Group (AAG) board member and is chair of the Chicago Public Health GIS Network. She received her Ph.D in Geography at Arizona State University, M.F.A in Writing from Roosevelt University, M.S. in GIS from John Hopkins University, and B.S. in Geology from the University of Illinois at Urbana-Champaign.

Novel Pattern Identification Methodologies for COVID-19 Medical Patient Data
2020-08-26 11:00 - 12:30 | Zoom Webinar
Abstract:
Novel pattern identification methodologies have been developed based upon mathematical methods used in theoretical physics. The resulting powerful algorithms can be utilized to find clusters in both numerical data tables of the attributes of things and of networks of connectivity among things. This research develops a transformative pattern identification algorithm to analyze COVID-19 patient data for cluster analysis in tabular numerical data tables, e.g., patient medical data files and in disease networks. A cloud-based clustering system is being designed and deployed to host multi-users’ large medical data submissions along with extensive user documentation and user tools for submission and resulting analysis. The initial prototype will be launched and tested in the fall of 2020 with extensive PRISMA COVID-19 personal medical encounter data seeking comorbidity and other regularities and patterns in the data.
About Speakers:
Dr. Joseph E. Johnson’s primary research interests are the applications of Lie algebras, groups, and Markov transformations to information theory with novel cluster and information spectral pattern identification in Big Data and networks and to relativistic quantum theory. He has been PI for over $12M in USC grants and was the past Associate Dean for Research in the College of Science and Mathematics.
Dr. Dezhi Wu is an associate professor at the Department of Integrated Information Technology. Her research interests are human-computer interaction, health IT, artificial intelligence, big data analytics, and Cybersecurity. Her research focuses on designing and creating novel user interfaces and applications for transformative user experiences to bridge the gaps between users and today’s evolving smart technologies. She was the recipient of the global technology award “AIS Technology Challenge Award,” and she is the former Chair for AIS Special Interest Group on Human-Computer Interaction (SIGHCI).
Register at: https://us02web.zoom.us/webinar/register/WN_YPkUh-UkRIieSd-UZI6XCg

Collaborative Data Conference with VA Health Care System and BDHSC
2020-08-18 13:00 - 17:00
Join us virtually for an opportunity to partake in a collaboration between UofSC Big Data Health Science Center and the VA Health Care System to discuss game changing healthcare analytics. Leaders from the VA and BDHSC will come together to give in-person presentations highlighting the innovative possibilities of big data approaches to healthcare research. The presentations will be broadcast live for your virtual viewing and participation.
When: August 18, 2020 1:00-5:00pm
Where: Virtually! Register for more information
How: Register at https://docs.google.com/forms/d/e/1FAIpQLSc0DakkPIFb45JhoIFVbAU-bKUUHWhFFZJBZDpCko-gbXpfNw/viewform

Leveraging the Science of Where for COVID-19 Response and Recovery
2020-06-23 12:00 - 13:30 | Zoom Webinar
The Big Data Health Science Center is hosting a 90-minute interactive webinar with representatives from ESRI in Redlands, CA. This webinar will focus on data visualization and analysis of COVID19 data using the ArcGIS platform. Pre-registration is required for the webinar (limited to 100 attendees). Interested in registering? Please contact Miranda Cole at MC95@mailbox.sc.edu for the registration details.
Webinar Title: Leveraging the Science of Where for COVID-19 Response and Recovery
Description: The response to the COVID-19 pandemic is providing significant insight into how to improve public health preparedness procedures, data, information products, and outcomes. Maps and spatial analysis are delivering greater understanding across disciplines and increasing awareness of the impacted populations, where resources are needed most, and improving communication to community stakeholders and the public. Esri’s ArcGIS software is helping communities ensure testing sites are accurately placed, personal protective equipment (PPE) inventories are tracked, communities are adhering to social distancing guidance and surge management is optimized. This webinar will demonstrate the solutions, analysis, models and data products that are organizations are leveraging around the world to respond to this pandemic. It will explore how organizations are using GIS to address the next phase of case resurgence, business continuity, re-opening of economies and recovery. Presenters will demonstrate specific software and solutions and field an extensive Q/A session with attendees.
About Speakers:
Jared Shoultz, M.A. is the Esri Health and Human Services Technical Lead drawing on over 20 years’ of related work experience. Prior to Esri, he was a Senior GIS Research Associate at the University of South Carolina Institute for Families in Society. He also spent 12 years at the South Carolina Department of Health and Environmental Control as a Deputy Director, Informatics Director and GIS Manager. His professional focus has been on the development, operation and GIS integration of enterprise public health and environmental information systems. He is part of the core Esri COVID-19 Response Team and has been working with public and private sector organizations at every level around the world to both respond and recover from the current pandemic.
Este Geraghty, MD, MS, MPH, CPH, GISP is the Chief Medical Officer at Esri, developer of the world’s most powerful mapping and analytics platform. She heads Esri’s worldwide health and human services practice and is passionate about transforming health organizations through a geographic approach. Previously, she was the deputy director of the Center for Health Statistics and Informatics at the California Department of Public Health. There she engaged in statewide initiatives in meaningful use, health information exchange, open data and interoperability. While serving as an associate professor of clinical internal medicine at the University of California (UC), Davis she conducted research on geographic approaches to influencing health policy and advancing community development programs. Geraghty is the author of numerous health and GIS peer reviewed papers and book chapters. She has lectured extensively around the world on a broad range of topics that include social determinants of health, open data, climate change, homelessness, access to care, opioid addiction, privacy issues and public health preparedness. She received her medical degree, master’s degree in health informatics, and master’s degree in public health from UC Davis. She is board certified in public health (CPH) and is also a geographic information system professional (GISP).

Big Data Analytics Workshop on Statistical and Machine Learning of Challenging Neuroimaging Datasets
2020-05-01 08:00 - 15:00 | Zoom Webinar
Due to COVID-19, this workshop has been postponed. We have switched to a virtual format and will now hold to workshop via Zoom Webinar.
Synopsis
The increasing volume and variety of neuroimaging datasets demand advanced modeling techniques to answer scientifically relevant questions such as causality and treatment effect. This workshop features novel statistical and machine learning approaches for analyzing high-dimensional brain signals, tree-shaped sulcal and gyral structures, and neurons.
Workshop Program

08.00 am – 09.00 am “Modeling Spectral Causality in a Brain Network”, Hernando Ombao, King Abdullah University of Science and Technology
9.00 am – 10.00 am “Heat Kernel Smoothing on Riemannian Manifolds and Its Application to Brain Images”, Moo K. Chung, University of Wisconsin-Madison
10.00 am – 11.00 am “Using Brain Imaging for Computer-Aided Prognosis of Stroke”, Chris Rorden, University of South Carolina
11.00 pm – 12.00 pm “Topological Signal Processing in Neuroimaging Studies”, Yuan Wang, University of South Carolina
12.00 pm – 13.00 pm “Machine Learning Applied to Neuroimaging Research in Neurology”, Leonardo Bonilha, Medical University of South Carolina
13.00 pm – 14.00 pm “Single-Trial Identification in fMRI Data: Applications to Representation of Affective States”, Svetlana Shinkareva, University of South Carolina
14.00 pm – 15.00 pm “Learning with Topological Priors and Constraints”, Chao Chen, Stony Brook University

Register at: https://zoom.us/webinar/register/WN_Hccotb-fScmqhCF0qXkakw

BDHSC Live Lecture: Big Data in the Time of COVID-19
2020-04-24 11:00 - 12:00
BDHSC invites UofSC Faculty/Staff and Students to a live lecture.

When: Apr 24, 2020 11:00 AM Eastern Time (US and Canada)
Topic: Live Lecture: Data Science in the Time of COVID-19
Register in advance for this webinar:
https://zoom.us/webinar/register/WN_xntSAcMVTW6KwxM1xt1M8w
After registering, you will receive a confirmation email containing information about joining the webinar.

“Bayesian Regression for Group Testing Data”
2020-02-26 11:00 - 12:30 | Room 140, Discovery I, 915 Greene Street, Columbia, SC, 29208, United States
Abstract:
Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of a disease. When individual covariate information is available (e.g., age, gender, number of sexual partners, etc.), a common goal is to relate an individual’s true disease status to the covariates in a regression model. Estimating this relationship is a nonstandard problem in group testing because true individual statuses are not observed and all testing responses (on pools and on individuals) are subject to misclassification arising from assay error. Previous regression methods for group testing data can be inefficient because they are restricted to using only initial pool responses and/or they make potentially unrealistic assumptions regarding the assay accuracy probabilities. To overcome these limitations, we propose a general Bayesian regression framework for modeling group testing data. The novelty of our approach is that it can be easily implemented with data from any group testing protocol. Furthermore, our approach will simultaneously estimate assay accuracy probabilities (along with the covariate effects) and can even be applied in screening situations where multiple assays are used. We apply our methods to group testing data collected in Iowa as part of statewide screening efforts for chlamydia, and we make user-friendly R code available to practitioners.
About the speaker:
Dr. Tebbs is Professor and Chair in the Department of Statistics, USC, an Associate Editor at Statistics in Medicine and a member of the Biostatistical Methods and Research Design Study Section of the NIH. Dr. Tebbs’ primary research interests are in the development of statistical methods for categorical data, especially aggregated or group tested data and their application in infectious disease screening, as well as in general biostatistical methods and problems involving ordering or shape restrictions. His research program is funded by the NIH.
Register at https://www.eventbrite.com/e/uofsc-bdhscs-monthly-seminars-tickets-93359339297

UofSC National Big Data Health Science Conference
2020-02-09 - 2020-02-11
UofSC National Big Data Health Science Conference
The University of South Carolina’s National Big Data Health Science Conference is a signature annual event of the UofSC Big Data Health Science Center (BDHSC). The theme for the 2020 Conference: Unlocking the Power of Big Data in Health – Partnerships among Industry, Government, and Academia. https://www.sc-bdhs-conference.org/
Register at https://www.sc-bdhs-conference.org/registration-information/

UofSC National Big Data Health Science Conference, Case Competition 2020
2020-02-07 - 2020-02-09
UofSC National Big Data Health Science Conference, Case Competition 2020
The Big Data Health Science Case Competition will be held between February 7th-9th 2020 before the University of South Carolina’s National Big Data Health Science Conference. The Big Data Case Competition is intended to provide enthusiastic teams of graduate and senior undergraduate students with the opportunity to apply their knowledge to the analysis of big datasets in healthcare.
https://www.sc-bdhs-conference.org/case-competition/
Register at https://www.sc-bdhs-conference.org/registration-information/

“Leveraging Social Media Technology to Improve Health and Emotional Well-being”
2020-01-29 11:00 - 12:30
“Leveraging Social Media Technology to Improve Health and Emotional Well-being”
Abstract:
Dr. Cavazos will present on systematic ways to explore social media posts about mental illness and substance misuse behaviors. Her research has implications for the delivery of online, accessible, and timely social media outreach that could facilitate new and more rapid responses in mental health promotion, prevention, and harm reduction initiatives.
About the speaker:
Dr. Patricia A. Cavazos is an Associate Professor in the Department of Psychiatry at Washington University School of Medicine in St. Louis. She is a clinically trained licensed psychologist who has been involved in biomedical research for over 15 years. Funded by the National Institutes of Health (NIH), her research program leverages social media technology to help those struggling with substance use disorders and mental illness.
Register at https://www.eventbrite.com/e/uofsc-bdhscs-monthly-seminars-tickets-88540030615

“Integrating Multi-Modal Biobehavioral Data to Treat Substance Use Disorders” Seminar
2019-10-30
“Integrating Multi-Modal Biobehavioral Data to Treat Substance Use Disorders” Seminar, Dr. Brett Froeliger, Medical University of South Carolina
Abstract:
The role of neural circuitry in motivation, reward and drug use will be discussed; along with associations between inhibitory control and relapse vulnerability. These topics will be discussed in the context of ongoing research on the potential value of 3 bio-behavioral treatments for substance use disorders.
About the speaker:
Brett Froeliger, PhD is an Associate Professor in the Department of Neuroscience, Neuroscience Graduate Program Director and Director of the Translational Research of Addiction and Integrative Neuroscience laboratory at the Medical University of South Carolina. He also holds an Adjunct Faculty position at Duke University Medical Center in the Department of Psychiatry and Behavioral Sciences and at the University of South Carolina in the Department of Pharmacy. His program of research has been continuously funded by NIH / NIDA since 2012 and is organized around using fMRI to evaluate the potential benefit of novel pharmacotherapies, behavioral interventions and, most recently, neural stimulation for treating substance use disorder pathophysiology.
Register at https://www.eventbrite.com/e/uofsc-bdhscs-monthly-seminars-tickets-76972594101

“Utilizing Big Data for Drug Discovery (Repurposing) to Improve Patient and Disease Outcomes” Seminar
2019-09-25
“Utilizing Big Data for Drug Discovery (Repurposing) to Improve Patient and Disease Outcomes” Seminar, Dr. Scott Sutton, UofSC
Abstract:
Drug repurposing holds the potential to bring existing medications with known safety profiles to new patient populations to accelerate drug development and lower treatment costs. Numerous examples exist for the identification of new indications for existing compounds and identified from unanticipated findings or focused efforts to the mechanism of action of a specific drug. In recent years, the need for new approaches to drug research and development, combined with the advent of big data repositories and associated analytical methods, has generated interest in developing systematic approaches to drug repurposing. In this presentation, we describe our approach to systematic repurposing and discuss the need for strategic collaborations to increase the likelihood of success in bringing existing molecules to new indications. The research examples utilized as big data examples include influenza, diabetes, Alzheimer’s, and Colorectal cancer.
About the speaker:
https://www.sc.edu/study/colleges_schools/pharmacy/faculty-staff/sutton_scott.php
Register at https://www.eventbrite.com/e/uofsc-bdhscs-monthly-seminars-tickets-70941739653

“Neuroimaging Data Collection in NIH StrokeNet Clinical Trials” Seminar
2019-08-28
“Neuroimaging Data Collection in NIH StrokeNet Clinical Trials” Seminar, Dr. Wenle Zhao, Medical University of South Carolina
Abstract:
Established in 2014, the NIH StrokeNet is designed to conduct large multicenter clinical trials for stroke acute treatment, recovery, and prevention. The Data Coordination Unit at the Medical University of South Carolina is the National Data Management Center for the StrokeNet, managing 8 concurrent stroke trials today. Most of these trials involve neuroimaging for eligibility and efficacy outcome assessments. This talk will present measures implemented for fast file uploading, PHI data cleaning, and neuroimaging central read and scoring, and wishes for automated neuroimaging processing.
About the speaker:
Dr. Zhao is a research professor in the Department of Public Health Sciences at the Medical University of South Carolina. He obtained his BS and MS in Mechanical Engineering at Zhejiang University, China, and his PhD in Biostatistics at MUSC. His research interest covers subject randomization algorithm in clinical trials and integrated information system for clinical trial data and operation management.
Register at https://www.eventbrite.com/e/the-uofsc-bdhscs-monthly-seminars-tickets-65242083829
