USC Big Data Health Science Center

Canceled: An Overview of the South Carolina Alzheimer’s Disease Registry
2022-08-26 11:00 - 12:00 UTC | Room 140, Discovery I, 915 Greene Street, Columbia, SC, 29208, United States
An Overview of the South Carolina Alzheimer’s Disease Registry by Dr. Maggi Miller (UofSC).
Register for in-person attendance (boxed lunch provided) at https://forms.gle/enuNSyA7owBGWvnx7.
Register for virtual attendance at https://us02web.zoom.us/webinar/register/WN_NTzdK2VXTQeihlx23Wvm1g.

Deadline for Pilot Project Proposals
2022-04-15
Announcement Release Data: January 10, 2022Application Receipt Date: April 15, 2022 by 5 p.m.Notification of Outcome: All applicants will receive notification by June 30, 2022Earliest Project Start Date: August 16, 2022
Purpose: The purpose of this RFP is to invite pilot project applications on the utilization of Big Data analytics in health-related research. The primary goals of this pilot project program will be to promote interdisciplinary collaboration in Big Data health science research and support meritorious applications that can leverage existing data to address critical issues related to health behavior, clinical care, healthcaredelivery, and population health. Interdisciplinary research that involves linking and integrating data sets from multiple sources are particularly encouraged. Existing data may include, but are not limited to electronic health records data, social media data, geospatial data, genomic data, or bio-nanomaterial data.
Background: Radical transformation is required in the US healthcare system to promote more effective and affordable approaches to personalized medicine and population health. Healthcare costs have outgrown the overall economy for several decades, yet health remains suboptimal, and the number of people with multiple chronic conditions continues to escalate. Life expectancy in the US has declined for three years in a row compared to other developed countries. The NIH-Wide Strategic Plan (2016-2020) asserts that our nation and the world stand at a unique moment of opportunity in biomedical research, and data science is an integral contributor. The generation of massive, rich data sets in healthcare (e.g., electronic health records, genomic data) and the emergence of advanced information, communication, and computational technologies — collectively referred to as “Big Data analytics” — offer an invaluable opportunity to improve the quality and efficiency of healthcare.
NIH issued its first Strategic Plan for Data Science in May 2018 and suggested that the Big Data approach will advance uniquely our understanding of disease prevention, identification, control, treatment, and delivery in the coming decades and will be key to reducing national and global health disparities. However, several critical gaps exist in utilizing such an approach, including the growing costs of managing data, “siloed” data resources with limited integration and interconnectivity, and an underutilization of Big Data approaches for clinical decision-making and research. A key reason for these gaps is a lack of data-science talent and limited leadership in the development, implementation, and evaluation of Big Data health analytics. The shortage of data scientists is projected to increase from 100,000 in 2012 to 240,000 by 2020. In response to this talent gap, academic institutions across the US, including several Ivy league universities and our aspirant peer institutions, have created data-science programs housed either in a Business or Engineering School, but few, if any, are currently focused on healthcare analytics.
With the support of UofSC excellence Initiative, the UofSC Big Data Health Science Center (BDHSC) aims to transform UofSC into a global leader in the focused field of Big Data health science analytics. To accomplish this mission, BDHSC has developed program activities around the following five strategic objectives: (1) Infrastructural and capacity building; (2) Professional development; (3) Community Engagement; (4) Academic training; and (5) Methodological advances. This pilot project program will serve the BDHSC mission by supporting and promoting interdisciplinary Big Data Health-related research across UofSC system. A total of 20 pilot projects were funded in 2020 and 2021 through a two-layer review process. The funded pilot projects can be found at BDHSC website: https://bigdata.sc.edu/
Research Objectives and Scope: The purpose of the pilot project program is to stimulate and promote interdisciplinary research in Big Data health sciences by supporting meritorious applications that utilize existing data sources in order to address critical issues related to health behavior, patient care, healthcare delivery, and population health. The program will support research that uses a variety of data sources, including electronic health records data, social media data, geospatial data, genomic data, bio-nanomaterial data, and other publicly available or acquirable data. The issues to be addressed by the pilot projects can also include a variety of health outcomes at individual, community, health system or population levels.
For questions regarding this RFP and proposal submission, please contact Ms. Miranda Nixon at mc95@mailbox.sc.edu; 803-777-5027

Full RFP Available Here
Click Here

Studying Substance use From Social Media Using Natural Language Processing and Machine Learning Methods
2022-03-23 11:00 - 12:00
Where: Virtual via Zoom (registration required)
How: Register at https://us02web.zoom.us/webinar/register/WN_f4s0gmlYTYO6h4S0sceSjw
Seminar Description: In this talk, I will outline the progress we have made over recent years in utilizing social media data via natural language processing (NLP) and machine learning methods for studying substance use. Substance use and the associated overdose epidemic has been continuing in the United States (US) for decades. Over 270 people on average die every day from substance-related overdoses. However, surveillance mechanisms are laggy and we only get to know about the state of the epidemic once substantial damage has already been done. For example, in February 2022, we only have provisional estimates from early 2021. To address this lag, we are working towards utilizing social media data for automatically tracking and estimating substance use trends (including opioids, benzodiazepines, and stimulants). The talk will mostly focus on our NIH/NIDA funded project that focuses on prescription drugs (R01DA046619). I will also present some updates from our research collaborations with the CDC, which focus specifically on substance use trends during COVID-19.
About the Speaker: Dr. Abeed Sarker is an assistant professor at the Department of Biomedical Informatics, School of Medicine, Emory University. He also serves as program faculty at the Department of Computer Science, Emory University and Department of Biomedical Engineering, Emory University and Georgia Institute of Technology. His research interests lie at the intersection of natural language processing, applied machine learning and social media. Much of his recent work has focused on utilizing social media big data to study substance use, including nonmedical use of prescription medications. He is currently leading a number of research projects in this space funded primarily by the National Institute on Drug Abuse of the National Institutes of Health, the Centers for Disease Control and Prevention and Emory University.

Deadline for R25 Fellow Applications
2022-03-14
Announcement Release Date: January 18, 2022Application Receipt Date: March 14, 2022 by 5 p.m. Notification of Outcome: All applicants will receive notification by April 30, 2022Earliest Project Start Date: May 16, 2022
Background: Supported by NIAID (R25AI164581-01), the UofSC Big Data Health Science Center (BDHSC) has been implementing a Big Data Health Science Fellow (“Big Data Fellow”) program since 2021. The multiple, massive, and rich Big Data streams in healthcare (e.g., electronic health records, mobile technologies, wearable devices, genomic data) and the emergence of advanced information and computational technologies (e.g., machine learning and artificial intelligence) offer an invaluable opportunity for applying innovative Big Data science research in NIAID focus areas of infectious diseases such as HIV/AIDS and COVID-19. Big Data science has the potential to identify high-risk individuals and communities and prioritize them for early biomedical or public health interventions, predict long-term clinical outcomes and disease progression, and evaluate public health policy impact. Key to addressing these complexities is a critical mass of health researchers with adequate knowledge, competencies, and skills to unlock important answers from Big Data to better understand, treat, and ultimately prevent these diseases and related comorbidities. However, there is a nationwide shortage of talent with such knowledge, competencies, and skills, especially in traditional academic settings. While junior faculty, as part of the generations of digital learners, have the greatest potential to develop their Big Data health science research agenda, many face multiple structural barriers to conducting Big Data science research. Such barriers include the lack of protected time to initiate new interdisciplinary Big Data research, opportunity to participate in funded Big Data research, and adequate mentoring. The Big Data Fellow program, as part of the BDHSC’s professional development mission, is designed to address these gaps and promote Big Data health science research at UofSC.
Program Goals and Aims: The program will recruit about 4 UofSC health science junior faculty per year and provide them with salary support (25%) to participate in the training program with the following specific aims:
Aim 1: Provide courses for competency and skills development in BDS research. Each trainee will complete 2 formal or informal courses (one per semester) in BDS areas that are appropriate for their background and research interests.Aim 2: Engage trainees in hands-on research and proposal development. Trainees will participate in ongoing NIH-funded Big Data research projects that utilize existing large data sources (e.g., NIH COVID-19 Cohort Collaborative [N3C] Data, SC statewide HIV and COVID-19 data and VA system-wide HIV and COVID-19 data). Aim 3: Provide trainees with rich mentoring experience in BDS research and professional development. Each trainee will be mentored by a team of NIAID-funded investigators who have complementary knowledge and skills from multiple domains (clinical medicine, public health, biostatistics, computing, geospatial science, social media, etc.,) and will engage in contextual mentoring and peer-to-peer mentoring.
Program Benefits and Support:The program will provide the following support to Fellow during the training year:

25% salary support for one year (subject to NIH salary cap)
Support for participation in grant writing bootcamp
Matched with a mentoring team
Participation in a funded Big Data research project
Support in NIH grant preparation and submission

Inquires: For questions related to various aspects of the Big Data Fellow program, please contact any of the following individuals:
Xiaoming Li, Ph.D., xiaoming@maillbox.sc.edu
Jiajia Zhang, Ph.D., jzhang@mailbox.sc.edu
Miranda Nixon, MA, mc95@mailbox.sc.edu

Full RFA Available Here
Click Here

National Big Data Health Science Conference
2022-02-11 08:00 - 2022-02-12 18:00
The University of South Carolina Big Data Health Science Center is pleased to announce that registration for its 3rd annual National Big Data Health Science Conference is now open! The theme of the conference, which will take place February 11-12, 2022, is “Unlocking the Power of Big Data in Health: Developing an Interdisciplinary Response for Health Equity”. Both in-person and virtual options for attendance are available. This conference will bring together leaders from academia, government, industry, and healthcare systems to focus on and forge new discussions about the role of interdisciplinary collaboration in Big Data applications and advancements in the health sciences. The agenda includes plenary addresses from 7 renowned experts, 6 in-depth breakout sessions ranging from electronic health records research to social media analysis (with over 30 contributing panelists from around the world), workshops in SAS Viya, computational modeling and more, and a wide variety of poster presentations.
Important information regarding the agenda, COVID-19 policies, and other FAQ can be viewed on the conference website at https://www.sc-bdhs-conference.org/
Register ($50) here, 2022 National Big Data Health Science Conference (touchnet.net).

Disparities in radon testing and the role of black residential segregation: A longitudinal study in an urban environment
2022-01-26 11:00 - 12:00
January 2022 Seminar: Disparities in radon testing and the role of black residential segregation: A longitudinal study in an urban environment by Dr. Dajun Dai. This seminar is hosted by the BDHSC Geospatial Core.
Seminar Description:Radon is a naturally released colorless and odorless radioactive gas. Exposure to indoor radon is the second leading cause of lung cancer after smoking. Short-term testing, the first line of protecting the public from potential residential exposure, remains exasperatingly low nationwide despite inexpensive and simple testing procedure. The goal of this study was to understand radon potential and promote geographically focused testing using geospatial technologies and a mixed-method approach. Working on a site in Atlanta categorized as the highest radon potential zone by the U.S. Environmental Protection Agency, this study addressed (1) the predictability of housing features and geology on indoor radon potential, (2) the characteristics of communities with low testing rates, and (3) the development of action-oriented health messaging that is culturally relevant to focused communities.
Speaker: Dr. Dajun Dai is a tenured professor at Department of Geosciences at Georgia State University (GSU) jointly appointed at GSU School of Public Health. He is an affiliated faculty member at the Injury Prevention Research Center at Emory University and Georgia Health Policy Center. His research addresses access to healthcare, neighborhood effects, social and physical difference and their implication on public health, planning, and public policy. His research has been supported by the World Health Organization, the National Institutes of Health, the National Science Foundation, the U.S. Geological Survey, and the Robert Wood Johnson Foundation, among others.
The in-person portion of this event has been canceledRegister for virtual attendance (via Zoom) here, https://bit.ly/3HyZVMN

Cytotoxic T-Cells: Nature's Assassins or something more?
2021-12-15 11:00 - 12:00
Date: Wednesday, December 15Time: 11:00am – 12:00pm (lunch provided to in-person attendees)Location: Discovery I, Room 331 (virtual option available)
Register for in-person (limited seating) at https://bit.ly/3d0yb6GRegister for virtual attendance at https://bit.ly/31d8ld6
Seminar Description:Cytotoxic (CD8+) T-cells are mainly known for their role in killing infected and cancerous cells however, recent evidence suggest that this cell population also plays a major role in the post-myocardial infarction (MI) remodeling process. Our lab has indicted that these cells likely have both a beneficial and detrimental role possibly due to temporal-dependent heterogeneity. Our goal is to dissect the role of cytotoxic T-cells over the post-MI time course and determine targets as a potential therapy to prevent development of heart failure.
Speaker Info:Dr. DeLeon-Pennell is an Assistant Professor in the Division of Cardiology at the Medical University of South Carolina (MUSC). She received her PhD in 2011 at Baylor University and was trained in cardiac physiology at the University of Texas Health Science Center San Antonio and the University of Mississippi Medical Center. Her research is focused on dissecting the interplay of pre-existing variables such as chronic inflammation, sex, and age on cardiovascular events. This includes dissecting the influence of the innate and adaptive immune system on cardiovascular remodeling, understanding the dynamics and function of bio-molecules involved in cardiac remodeling, and utilizing the knowledge acquired to develop therapeutic strategies to prevent, slow, or reverse the progression to heart failure.

Employing Social Media to Improve Mental Health: Pitfalls, Lessons Learned, and the Next Frontier
2021-11-17 11:00 - 12:00
The BDHSC Social Media Core is hosting Dr. Munmun De Choudhurry for a virtual seminar titled “Employing Social Media to Improve Mental Health: Pitfalls, Lessons Learned, and the Next Frontier”.
The seminar will be held virtually via Zoom on November 17, 2021 from 11:00am-12:00pm.
Register at https://bit.ly/3bucMln
About the Seminar: Social media data is being increasingly used to computationally learn about and infer the mental health states of individuals and populations. Despite being touted as a powerful means to shape interventions and impact mental health recovery, we understand little about the theoretical, domain, and psychometric validity of this novel information source, or its underlying biases, when appropriated to augment conventionally gathered data, such as surveys and verbal self-reports.
This talk presents a critical analytic perspective on the pitfalls of social media signals of mental health, especially when they are derived from “proxy” diagnostic indicators, often removed from the real-world context in which they are likely to be used. To overcome these pitfalls, this talk presents results from two case studies (forecasting schizophrenia relapse and populational-level rates of suicide fatalities), where machine learning algorithms to glean mental health insights from social media were developed in a context-sensitive and human-centered way, in collaboration with domain experts and stakeholders.
The talk concludes with discussions of the path forward, emphasizing the need for a collaborative, multi-disciplinary research agenda while realizing the potential of social media data and machine learning in mental health — one that incorporates methodological rigor, ethics, and accountability, all at once.
About the Speaker: Munmun De Choudhury is an Associate Professor of Interactive Computing at Georgia Tech. Dr. De Choudhury is best known for laying the foundation of a line of research that develops computational techniques to responsibly and ethically employ social media in understanding and improving our mental health. To do this work, she adopts a highly interdisciplinary approach, combining social computing, machine learning, and natural language analysis with insights and theories from the social, behavioral, and health sciences. Dr. De Choudhury has been recognized with the 2021 ACM-W Rising Star Award, 2019 Complex Systems Society – Junior Scientific Award, numerous best paper and honorable mention awards from the ACM and AAAI, and features and coverage in popular press like the New York Times, the NPR, and the BBC. Earlier, Dr. De Choudhury was a faculty associate with the Berkman Klein Center for Internet and Society at Harvard, a postdoc at Microsoft Research, and obtained her PhD in Computer Science from Arizona State University.

BDHS Conference: Abstract Submission Deadline
2021-11-01
The Big Data Health Science Center is seeking abstract submissions for its 3rd annual National Big Data Health Science Conference. The theme of the conference is “Unlocking the Power of Big Data in Health: Developing an Interdisciplinary Response for Health Equity“. The conference will be held in Columbia, SC (virtual option available) on February 11-12, 2022.
Research and program-based abstracts in the areas of AI, COVID-19, EHR, social media data, genomics data, geospatial data, infrastructural and capacity development, professional development, community/industry engagement, academic training, and methodological advances are desired. Students are encouraged to submit abstracts for poster presentations.
The deadline for submissions is November 1, 2021. For more information or to submit your abstract, visit https://www.sc-bdhs-conference.org/abstract-2022/

Geographic Information System (GIS) Across the Biomedical Research Continuum
2021-10-27 11:00 - 12:00
Date: Wednesday, October 27Time: 11:00am – 12:00pm (lunch provided to in-person attendees)Location: Discovery I, Room 331 (virtual option available)
Register for in-person (limited seating) at https://bit.ly/2YrXtXz Register for virtual attendance at https://bit.ly/2ZKC5x1
Seminar Description:Increasingly, scientific evidence reinforces the understanding that human health problems derive from a wide array of intervening environmental circumstances that operate on diverging spatial and temporal scales. The Wilde’s exposome model addressed this “environmental health” phenomenon by emphasizing the need for a more complete environmental exposure assessment—to complement the genome, and therefore provide a comprehensive description of lifelong exposure history. In some significant ways, the core framework of the Wilde’s exposome model reinforces the growing importance of applying GIS techniques and spatial epidemiology into research activities across the biomedical research continuum.
Using the exposome model as its backdrop, this talk will discuss selected ongoing GIS-related research projects and highlight the opportunities and challenges for integrating geospatial analysis methods into the biomedical research continuum. Overall, the talk is expected to demonstrate how GIS approaches offer the opportunity to enhance the modeling of health outcomes and provide better clinical care decision support.
Speaker Info:Dr. Abi Oluyomi is an assistant professor of medicine in the Section of Epidemiology and Population Sciences in the Department of Medicine at Baylor College of Medicine. He is an environmental health epidemiologist with research experience that cuts across multiple fields of environmental design and environmental health science, and expertise in geospatial exposure assessment and epidemiology. His research interests center on the assessment of non-chemical and chemical environmental stressors and investigating the pathways through which these stressors are related diseases and their risk factors. He serves in different research capacities at Baylor College of Medicine and nationally. At Baylor, he leads the BCM Biomedical Geospatial Analytics and Modeling Lab – a Lab that routinely provides specialist geographic information system (GIS) services to researchers across the Texas Medical Center on a wide range of health topics.

Big Data Seminar Series: Integrating Simulation Modeling with Big Data in Oncology
2021-09-22 11:00 - 12:30
Join us for the first seminar of the semester. Dr. Shi-Yi Wang of Yale University will present “Integrating Simulation Modeling with Big Data in Oncology”. This event is hosted by the BDHSC Electronic Health Records core. Both in-person and virtual attendance available. Boxed to-go lunch will be provided to in-person attendees. In-person space is limited.
Register for in-person attendance: https://bit.ly/2XTGz3D
Register for virtual attendance: https://bit.ly/3mz6Ezm
Seminar Description: The linkage of the Surveillance, Epidemiology and End Results (SEER) program of cancer registries and Medicare claims results in a unique population-based source of information. To date, there are approximately 5,000 SEER-Medicare manuscripts. Additionally, simulation modeling has frequently been used to assess interventions in complex aspects of health care. Combining simulation and secondary data analysis, researchers can answer questions that are not easily satisfied via conventional clinical observational studies.
About the Speaker: Dr. Shi-Yi Wang is an Associate Professor in the Yale School of Public Health’s Department of Chronic Disease Epidemiology and Public Health Modeling Concentration, and a faculty member of the Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center. His primary interests focus on outcomes research and decision science. He is interested in combining systematic literature reviews, secondary data analyses, and simulation modeling to examine issues that are critical to clinicians and policy makers’ decision making.

BDHSC 2020 Pilot Project Showcase
2021-04-28 11:00 - 12:00
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.
