DBMI Special Seminar Series: Toward Diversity, Equity, and Inclusion in Informatics, Health Care, and Society

The Columbia Department of Biomedical Informatics announced a series of talks entitled “DBMI Special Seminar Series: Toward Diversity, Equity, and Inclusion in Informatics, Health Care, and Society.

These talks, which began during the 2021 spring semester and are open to the public, focus on informatics research topics related to diversity, equity, and inclusion and be part of the weekly DBMI Seminar, a 1-credit course for DBMI students who can benefit from hearing new methods of research from speakers from both academia and industry. 

Seminars that are part of this series will be posted below, while upcoming seminars will be listed on the DBMI Seminar page.

Previous Seminars

Juan Banda, Assistant Professor of Computer Science, Georgia State University

Talk title: Are phenotyping algorithms fair for underrepresented minorities within older adults?

Watch The Presentation Here

Abstract: The widespread adoption of machine learning (ML) algorithms for risk-stratification has unearthed plenty of cases of racial/ethnic biases within algorithms. When built without careful weightage and bias-proofing, ML algorithms can give wrong recommendations, thereby worsening health disparities faced by communities of color. Biases within electronic phenotyping algorithms are largely unexplored. In this work, we look at probabilistic phenotyping algorithms for clinical conditions common in vulnerable older adults: dementia, frailty, mild cognitive impairment, Alzheimer’s disease, and Parkinson’s disease. We created an experimental framework to explore racial/ethnic biases within a single healthcare system, Stanford Health Care, to fully evaluate the performance of such algorithms under different ethnicity distributions, allowing us to identify which algorithms may be biased and under what conditions. We demonstrate that these algorithms have performance (precision, recall, accuracy) variations anywhere between 3 to 30% across ethnic populations; even when not using ethnicity as an input variable. In over 1,200 model evaluations, we have identified patterns that indicate which phenotype algorithms are more susceptible to exhibiting bias for certain ethnic groups. Lastly, we present recommendations for how to discover and potentially fix these biases in the context of the five phenotypes selected for this assessment. 

Bio: Dr. Juan M. Banda at his GSU lab, Panacea Lab, works on building machine learning, and NLP methods that help to generate insights from multi-modal large-scale data sources, with applications to precision medicine, medical informatics, as well as other domains. His research interests are not limited to structured data, he is also well-versed in extracting terms and clinical concepts from millions of unstructured electronic health records and using them to build predictive models (electronic phenotyping) and mine for potential multi-drug interactions (drug safety). Dr. Banda’s has published over 70 peer reviewed conference and journal papers and serves as an editorial board member of the Journal of the American Medical Informatics and Frontiers in Medicine – Translational Medicine, and a reviewer for JBI, nature Digital Medicine, nature Scientific Data, nature Protocols, PLOS One, and several other leading journals. Prior to being an assistant professor of Computer Science at Georgia State University, Dr. Banda was a postdoctoral scholar, then a research scientist at Stanford’s center of Biomedical Informatics. He is an active collaborator of the Observational Health Data Sciences and Informatics, and his work has been funded by the Department of Veteran Affairs, National Institute of Aging as well as NASA, NSF and NIH, and serves as a PC member and chair for several conferences and workshops including ICML, NeurIPS, FLAIRS, IEEE Big Data, among others.

Charisse Madlock-Brown, Assistant Professor, University of Tennessee Health Science Center

Title: Multimorbidity Patterns Across Race/Ethnicity Stratified by Age and Obesity: A Cross-sectional Study of a National US Sample

(Due to the ongoing research, this seminar was not recorded)

Objectives: The objective of our study is to assess differences in prevalence of multimorbidity by race. 

Methods: We applied the FP-growth algorithm on middle-aged and elderly cohorts stratified by race, age, and obesity level. We used 2016-2017 data from the Cerner HealthFacts® Electronic Health Record data warehouse.  We identified disease combinations that are shared by all races/ethnicities, those shared by some, and those that are unique to one group for each age/obesity level. 

Results: Our findings demonstrate that even after controlling for age and obesity, there are differences in multimorbidity prevalence across races. There are multimorbidity combinations distinct to some racial groups—many of which are understudied. Some multimorbidities are shared by some but not all races. African Americans presented with the most distinct multimorbidities at an earlier age. 

Discussion: The identification of prevalent multimorbidity combinations amongst subpopulations provides information specific to their unique clinical needs. 

Irene Dankwa-Mullan MD MPH, Chief Health Equity Officer, IBM Watson Health, IBM Corporation

Title: Identifying and Leveraging Public Data Sources with Social Determinants of Health Information for Population Health Informatics Research 

Abstract: Social determinants of health (SDOH) account for many health inequities. Data sources traditionally used in informatics research often lack SDOH, and, when available, SDOH may be difficult to leverage given it’s lack of specificity and lack of structured information. In this presentation, I will share the initial phases of work that we are doing around leveraging SDoH data – for health equity research – addressing some of the informatics challenges leveraging social determinants of health data to inform population health or inform health services research. I will discuss a case study using a machine learning clustering algorithm to uncover region-specific sociodemographic features and disease-risk prevalence correlated with COVID-19 mortality during the early accelerated phase of community spread.

Megan Threats, PhD, MSLIS, Assistant Professor of Library and Information Sciences 

Title: Toward health justice in informatics: a community-based, intersectional approach to HIV informatics intervention development 
 
Abstract: June 2021 will mark 40 years since the first cases of what would later become known as acquired immunodeficiency syndrome (AIDS) were reported in the United States. Despite groundbreaking biomedical advancements in HIV prevention and treatment, the HIV/AIDS epidemic continues to disproportionately affect sexual and gender minority communities of color. In this talk, I will discuss the development of an HIV informatics intervention aimed at reducing inequities in linkage and retention in HIV prevention and care among sexual minority Black men in the South. I will present strategies for leveraging informatics to achieve health justice in the fight to end AIDS.