USC Big Data Health Science Center

Novel Pattern Identification Methodologies for COVID-19 Medical Patient Data
2020-08-26 11:00 - 12:30 UTC | 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
