CEH Seminar Series

Estimating the Prevalence of Multiple Chronic Diseases via Maximum Entropy

Dr. Hari Balasubramanian, Associate Professor of Industrial Engineering, University of Massachusetts, Amhurst

Host: Professor Sanjay Mehrotra

August 8th, 2022

11 a.m. to 12 p.m CDT



Abstract: Patients with multiple chronic conditions, also known as multi-morbidity in the clinical literature, have a disproportionate impact on the U.S. healthcare system. According to a 2017 report by RAND, 19% of Americans (around 60 million individuals) had four or more chronic conditions, and they account for more than 50% of total healthcare expenditure. A baseline predictive model for the probabilities of co-occurring conditions is essential for quantifying epidemiological associations between condition groups, resource planning for targeted interventions, and driving decision support for personalized medicine. However, MCC patients exhibit significant heterogeneity in chronic condition combinations, and the number of individuals in a disease dataset is usually small compared to the number of possible disease combinations. Therefore, simple maximum-likelihood estimates of disease co-occurrence will erroneously assign zero probabilities to disease combinations that are missing from the dataset but are likely to occur in the larger population. In this work, we combine maximum-entropy optimization, data mining, and machine learning techniques to create an algorithm, called MaxEnt-MCC, for estimating the prevalence of chronic diseases in a population in the face of sparse data. In a case study using Medical Expenditure Panel Survey (MEPS) data, we show how MaxEnt-MCC can be used to predict previously unobserved but likely disease combinations, quantify associations between groups of chronic conditions, and estimate healthcare costs in a principled manner.

 This research is joint work with Peter Haas (Professor, College of Information and Computer Sciences, University of Massachusetts, Amherst) and Pracheta Amarnath (PhD student, College of Information and Computer Sciences, University of Massachusetts, Amherst).

Bio: Dr. Hari Balasubramanian is Associate Professor of Industrial Engineering at the University of Massachusetts, Amherst. He received his doctoral degree at Arizona State University in 2006. After graduation, Dr. Balasubramanian spent two years as a Research Associate at Mayo Clinic in Rochester, Minnesota before joining the University of Massachusetts in 2008. His research interests are in operations research applied to healthcare. Specific applications have included capacity planning and scheduling in outpatient, inpatient and emergency room settings. Dr. Balasubramanian's work has been supported by grants from the National Science Foundation including a National Science Foundation CAREER award (2013-2019) focused on improving primary care delivery. His papers have been published in both operations research as well as clinical journals. His recent work is on modeling the impact of care interventions on patients with complex medical and social needs.



This seminar series welcomes a broad range of healthcare modeling research topics such as healthcare operations, medical decision making, health policy, and health analytics. The series is organized by Sanjay Mehrotra (Northwestern University)Sait Tunc (Virginia Tech)Qiushi Chen (Pennsylvania State University).
The advisory board of the Year 2022 includes Mark Van Oyen (University of Michigan)Maria Mayorga (North Carolina State University), and Timothy Chan (University of Toronto).

Seminars are typically held from 12PM - 1PM CT on the fourth Friday of every month.  More information on the series, as well as links to past seminars, can be found here.


Project Eva: Designing and Deploying the Greek COVID-19 Testing System

Dr. Vishal Gupta, USC Marshall School of Business

June 24th, 2022

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Abstract: On July 1st, 2020, members of the European Union gradually lifted earlier COVID-19 restrictions on non-essential travel. In response, we designed and deployed “Eva” – a novel reinforcement learning system – across all Greek borders to identify asymptomatic travelers infected with SARS-CoV-2. Eva allocates Greece’s limited testing resources based on demographic characteristics and results from previously tested travelers to (i) limit the influx of new cases and (ii) provide real-time estimates of COVID-19 prevalence to inform border policies. Counterfactual analysis shows that Eva identified 1.85x as many asymptomatic, infected travelers as random surveillance testing, with up to 2-4x as many during peak travel. Moreover, Eva identified approximately 1.25-1.45x as many infected travelers as policies that require similar infrastructure as Eva, but make allocations based on population-level epidemiological metrics (cases/deaths/positivity rates) rather than reinforcement learning. This talk discusses some of the main design decisions behind Eva, the key elements of the reinforcement learning algorithm, and the measured impact of the system in the summer of 2020.

Bio: Vishal Gupta is an Associate Professor of Data Sciences and Operations at the USC Marshall School of Business. Before joining USC, Vishal Gupta completed his B.A. in Mathematics and Philosophy at Yale University, graduating Magna Cum Laude with honors, and completed Part III of the Mathematics Tripos at the University of Cambridge with distinction. He then spent four years working as a “quant” in finance at Barclays Capital, focusing on commodities modeling, derivatives pricing, and risk management.

Eventually, Vishal realized how much he missed working towards a larger mission of impact, and left the private sector to complete his Ph.D. in Operations Research at MIT in 2014.

Vishal’s research focuses on data-driven decision-making and optimization, particularly in settings where data are scarce.Such settings are common in applications that rely on personalization (like precision healthcare) and real-time decision-making (like risk management). Consequently, his research spans a wide variety of areas including revenue management, education, healthcare, and artificial intelligence. Vishal has received a number of recognitions for his work, including the Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research, the Pierskalla Best Paper Prize, the Jagdish Sheth Impact of Research on Practice Award.





Optimizing Implementation Science Applications in Community-Engaged Research


Sabira Taher, PhD, MPH

June 13, 2022

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A growing demand for the uptake of evidence-based interventions (EBIs) that meet the needs of diverse populations has resulted in the adoption of community-engaged research in dissemination and implementation (D&I) studies. The integration of these two paradigms shows potential for the effective diffusion of EBIs in real-world settings. Yet, more work is needed to fully optimize the benefits of this combined approach. In this seminar, I will highlight the community-engaged and implementation science approach I utilized in feasibility studies for social needs screening and cardiovascular disease management interventions. Implementation strategies and recommendations were tailored to the needs of urban, minority and immigrant communities, and relied heavily on the specific context and community input. I will conclude with my vision to advance implementation science applications in health equity research across Northwestern University. I hope to enhance current community engagement activities and foster new D&I research collaborations with a multi-year research plan.

Sabira Taher, PhD, MPH
T32 Cardiovascular Disease Epidemiology Research Fellow at Northwestern University Feinberg School of Medicine
Adjunct Clinical Professor, College of Applied Health Sciences at the University of Illinois at Chicago.

Dr. Taher's research focuses on equitable implementation and wide-scale dissemination of behavioral interventions adapted for low-income and immigrant populations. Dr. Taher applies principals of implementation science and community-engaged research to identify and address the multidimensional issues that affect food access, dietary patterns and cardiovascular disease risk factors in diverse populations.



Past Events


CEH Seminar Series: Prioritizing Hepatitis C Treatment Decisions in U.S. Prisons

Turgay Ayer, an assistant professor at Georgia Tech, spoke at the CEH Seminar Series on Wednesday, October 5, 2016.
Learn more


CEH Seminar Series: Online Decision-Making with High-Dimensional Covariates

Mohsen Bayati, an associate professor at Stanford University, spoke at the CEH Seminar Series on Wednesday, September 28, 2016.
Learn more