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Eight-segment panorama of Chicago, Illinois, as viewed from North Avenue Beach

Panelist

Pinar Keskinocak, PhD

Title: Modeling and Analytics for Infectious Diseases and Covid-19

Biography: Pinar Keskinocak is the William W. George Chair and Professor in the School of Industrial and Systems Engineering and the co-founder and Director of the Center for Health and Humanitarian Systems at Georgia Institute of Technology.

Dr. Keskinocak’s research focuses on the applications of quantitative methods and analytics to have a positive impact in society, particularly in healthcare and humanitarian systems. Her recent work has addressed a broad range of topics such as infectious disease modeling, evaluating intervention strategies, and resource allocation; catch-up scheduling for vaccinations; decision-support for organ transplant; hospital operations management; and disaster preparedness and response. She has worked on projects with a variety of governmental and non-governmental organizations, and healthcare providers, including American Red Cross, CARE, Carter Center, CDC, Children’s Healthcare of Atlanta, Emory Healthcare, Grady Hospital, and Task Force for Global Health. Her work has been published in numerous peer-reviewed journals.

Dr. Keskinocak is an INFORMS Fellow, served as the 2020 President of INFORMS, and has served in various other roles within the society over the years, including INFORMS Secretary, INFORMS Vice President for Membership and Professional Recognition, President of the Women on OR/MS Forum, President of the Public Sector OR Section, and Department Editor for Operations Research. She has also been an active member of other professional societies including IISE.

Abstract: Infectious disease prevention and control involve numerous challenges; along with the medical and biological aspects, the decisions and actions also need to consider resources, behaviors, societal impact, and many other factors. Mathematical models can help our understanding of disease progression in individuals and spread/prevalence across the population, as well as the impact of pharmaceutical and non-pharmaceutical interventions. When there are limited resources for interventions, e.g., for prevention or treatment, modeling can also help in resource allocation, e.g., identifying which combinations of interventions would be most effective for different geographic regions, subpopulations, or individuals. In this presentation, we will share examples of such models and insights about how they might inform decisions in practice.

Yuan Luo, PhD

Title: Simulation of Scarce Resource Allocation in Critically Ill Patients with COVID-19

Biography: Yuan Luo, PhD, is an Associate Professor at Department of Preventive Medicine, Division of Health & Biomedical Informatics at Feinberg School of Medicine in Northwestern University. He is Chief AI Officer at Northwestern University Clinical and Translational Sciences Institute and Institute for Augmented Intelligence in Medicine. His research interests include machine learning, natural language processing, time series analysis, computational phenotyping and integrative genomics, with a focus on biomedical applications. He won the American Medical Informatics Association (AMIA) New Investigator Award in 2020. He has published over 100 articles in leading journals (e.g., Nature Medicine) and top AI venues (e.g., AAAI). He is currently an editor with JAMIA Open, JBI, Plos One, JHIR. He served on AMIA Membership and Outreach Committee.

Abstract: The coronavirus disease 2019 (COVID-19) pandemic has prompted policymakers to develop widely varying allocation protocols to allocate scarce healthcare resources. We determine the intended and unintended consequences of scarce critical care resource allocation protocols. Our study design uses Monte Carlo simulation of intensive care unit (ICU) bed rationing to evaluate the performance of multiple allocation protocols in real world use. Our cohorts consits of critically ill adult patients with COVID-19 from two tertiary care academic hospitals and a six-community hospital network in the Chicagoland area.

Fei Wang, PhD

Title: Data-Driven Subphenotyping for Characterizing the Clinical Heterogeneity of COVID-19

Biography: Fei Wang is an Associate Professor in Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University. His major research interest is data mining, machine learning and their applications in health data science. He has published more than 250 papers on the top venues of related areas such as ICML, KDD, NIPS, CVPR, AAAI, IJCAI, etc. His papers have received over 16,000 citations so far with an H-index 62. His (or his students’) papers have won 7 best paper (or nomination) awards at top international conferences on data mining and medical informatics. His team won the championship of the NIPS/Kaggle Challenge on Classification of Clinically Actionable Genetic Mutations in 2017 and Parkinson's Progression Markers' Initiative data challenge organized by Michael J. Fox Foundation in 2016. Dr. Wang is the recipient of the NSF CAREER Award in 2018, as well as the inaugural research leadership award in IEEE International Conference on Health Informatics (ICHI) 2019. Dr. Wang’s Research has been supported by NSF, NIH, ONR, PCORI, MJFF, AHA, Amazon, etc. Dr. Wang is the past chair of the Knowledge Discovery and Data Mining working group in American Medical Informatics Association (AMIA). Dr. Wang is a fellow of AMIA.

Abstract: COVID-19 has demonstrated extreme heterogeneity in its clinical manifestations. Understanding the patterns of such clinical heterogeneity can shed light on the distinct underlying pathophysiological mechanisms and inform effective targeted treatments. In this talk I will introduce two works for this purpose, which characterizes the subphenotypes of COVID-19 patients at the time of confirmation and the longitudinal progression subphenotypes for critically ill COVID-19 patients after mechanical ventilation. In-depth discussions on the clinical implications and future directions will also be presented.

Important Dates
Call for Submission Deadline Notification of Acceptance
Papers April 30 May 29
Workshops April 7 April 14
Tutorials April 30 May 7
Highlights May 10 June 14
Posters May 14 May 27
Late-break Posters May 20 June 15

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