USC Big Data Health Science Center

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

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.

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

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

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: 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

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

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.

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.

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/

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.

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.

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

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).

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

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 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

Canceled: An Overview of the South Carolina Alzheimer’s Disease Registry
2022-08-26 11:00 - 12:00 | 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.

NIMH Division of AIDS Research Funding Opportunities and Priorities in Data Science
2022-09-28 11:00 - 12:00 | Koger Center for the Arts, Gallery Room, 1051 Greene St, Columbia, SC, 29201, United States
NIMH Division of AIDS Research Funding Opportunities and Priorities in Data Science by Dr. Lori Scott-Sheldon (Chief of Data Science and Emerging Methodologies in HIV at the National Institute of Mental Health).
Register at https://forms.gle/RbVP3yd44BNwf5uL9
Boxed lunch provided on first come first served basis.
No virtual option available but check our website in the weeks following for a recording.

Big Data Health Science Center Annual Retreat 2022
2022-09-30 09:00 - 13:00 | Campus Room, Capstone Hall, 902 Barnwell St, Columbia, SC, 29208, United States
ePlease see the below for information about the upcoming BDHSC retreat and networking luncheon (9/30). This event is intended for BDHSC affiliates and stakeholders to gather and discuss extramurally funded research, opportunities for collaboration, and challenges in data acquisition. Students must seek permission prior to registering. BDHSC faculty affiliates and core members are encouraged to attend.
Register (in-person only) at BDHSC Annual Retreat Registration (google.com).

BDHSC Pilot Project Showcase
2022-10-28 11:00 - 12:00 | Room 140, Discovery I, 915 Greene Street, Columbia, SC, 29208, United States
Join us as we hear about the projects of four 2021 Big Data Health Science Center Pilot Project Recipients!
Friday, October 28 from 11-12pm
In-person: Discovery 1, Room 140 and virtual option available
Registration required. Register at in-person: https://docs.google.com/forms/d/e/1FAIpQLScNdjmGrrp3u2bfyZtsGMvvV7VAJ7T7Y0FXT2qm0jSOvo2w9w/viewform?usp=sf_link
Virtual: https://us02web.zoom.us/webinar/register/WN_znlpwqA5SKuS7277fz2lrg or by scanning the QR code below.
Lunch provided.
Dezhi Wu, Ph.D., College of Engineering and Computing
Exploring Sentiment and Communication Exchange Patterns of Substance Use Disorder (SUD) Associated with Pregnant Women on Twitter Before and During COVID-19 Pandemic
Substance use among pregnant women is a significant public health concern. Recent studies have shown an alarming increase in substance use; however, it is still unknown how the COVID-19 pandemic has impacted vulnerable pregnant women. We applied NLP, text mining and machine learning techniques to extract just-in-time substance use (SU) data among pregnant women on Twitter, and then explored their communication patterns, risky health perceptions, sentiment, and maternal and fetal health outcomes. Our study findings are informative to perinatal healthcare practice and related public health policy changes.
Peiyin Hung, Ph.D., Arnold School of Public Health
Rural and Racial Disparities in Maternal Telehealth Health Care Access during the COVID-19 Pandemic.
Ensuring equitable telehealth uptake in prenatal care is critical for the optimal outcomes of newly developed prenatal care guidelines. This study aims to quantify the trends of prenatal care via telehealth before and during the COVID-19 pandemic, and assess the variations by maternal race, ethnicity, and residence. We used a statewide Medicaid claims data for Medicaid-insured women in South Carolina to assess prenatal telehealth uptake before and during the COVID-19 pandemic. Using an interrupted time series approach, we found that prenatal care visits via telehealth uptake increased substantially at the beginning of the COVID-19 pandemic, decreased within a few months, but remained more prevalent than the pre-pandemic rates. This study highlights the disproportionate telehealth access among rural women and women from racial minority groups who have historically faced barriers to accessing prenatal care.
Greg Trevors, Ph.D., College of Education
Identifying Optimal Vaccine Promotion Messages for Vulnerable Subgroups from Large-Scale Gamified Interventions
The long-term goal of this project is to inform the design of subsequent digital health promotion interventions that will dynamically personalize to participants based on empirical insights gained from machine learning analyses on our large-scale digital intervention data. To accomplish these long-term goals, the immediate objectives in this proposal are (1) identify distinct vaccine hesitancy subgroups from our prior intervention participants; and (2) identify optimal content that is associated with an increase likelihood of healthy behavioral intentions and attitudes, including vaccine intent and confidence, for each VH subgroup.
Chen Liang, Ph.D., Arnold School of Public Health
Deep Phenotyping individuals with PASC using a Graph representational model of S3C
Studies found 47-87% of patients with persistent symptoms and multi-system organ dysfunction after the acute phase of COVID-19, denoted as Post-Acute Sequela of SARS-CoV-2 infection (PASC). Leveraging a biomedical informatics and data science approach, we will design an innovative Graph representational model on top of a multi-center clinical dataset [South Carolina COVID-19 Cohort (S3C), NIH/NIAID R01A127203-4S1]. Using this Graph model, we will develop and pilot test a new semi-supervised machine-learning deep phenotyping algorithm to identify individuals with PASC and characterize their electronic phenotypes. This study will result in a clinically plausible, interpretable, generalizable, and high-throughput deep phenotyping algorithm application for identifying possible PASC cases at COVID-19 recovery clinics, supporting patient referral, outreach, and therapeutics development.

Alzheimer's Disease Registry Seminar
2022-11-16 10:30 - 11:30 | 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 (USC).
Register at https://forms.gle/LVZDeiz1SmtNHc19A.

Learning Individualized Treatment Rules with Many Treatments
2022-12-07 11:00 - 12:00 | Virtual
Please join us on December 7 for a virtual seminar “Learning Individualized Treatment Rules with Many Treatments” by Dr. Yufeng Liu, the University of North Carolina at Chapel Hill. This seminar is hosted by our Electronic Health Records Core.
Registration required. Please REGISTER HERE

Deadline for Abstracts Submission - 2023 Annual Big Data Health Science Conference
2022-12-12
The Big Data Health Science Center is seeking research and program-based abstracts for oral and poster presentations responsive to its 4th annual conference theme, “Unlocking the Power of Big Data in Health: Translating Data Science into Program Development and Implementation”. This conference will be held in-person at the Pastides Alumni Center in Columbia, SC on February 10-11,2023. Accepted abstracts will be published in conference proceedings Research and program-based abstracts in the areas of infectious diseases, AI for Sensing and Diagnosis, EHR, social media data, genomics analysis, geospatial research, GIS, infrastructural and capacity development, professional development, community/industry engagement, academic training, and methodological advances are desired.
Learn more at https://www.sc-bdhs-conference.org/.

BDHSC Pilot Project Showcase Part 2
2023-01-25 13:00 - 14:00 | Room 140, Discovery I, 915 Greene Street, Columbia, SC, 29208, United States
Please join us for a Big Data seminar showcasing 4 of our 2021 pilot project recipients. Speakers will discuss their research questions, data, methods, and future directions. This event is free and boxed lunch will be provided on a first come first served basis. Register at https://forms.gle/RoryZogDVET3FCRF9.

National Big Data Health Science Student Case Competition 2023
2023-02-03 - 2023-02-05 | Virtual

National Big Data Health Science Conference 2023
2023-02-10 - 2023-02-11 | Pastides Alumni Center, Columbia, SC, 900 Senate St, Columbia, SC, 29201, United States

Deadline for NIH-funded T35 Research Training Program Applications
2023-02-15

Deadline for 2023 Health GIS Scholars Program
2023-03-01

An Overview of the Federal Statistical Research Data Centers
2023-03-22 12:00 - 13:00 | Zoom Webinar
