DeepTrack: An ML-based Approach to Health Disparity Identification and Determinant Tracking for Improving Pandemic Health Care

IEEE Computer Society Team
Published 02/07/2022
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ML identifying disparities in healthcareFor week two of Black History Month, we take a deeper look at how those within the computing space are redefining health outcomes for patients from underrepresented groups.

Through technology, data, and analysis, we can better understand the successes as well as the challenges we all must face together. Not only do we need to explore and celebrate the accomplishments of the Black community, but we must also address the issues that keep our society from being truly equitable.



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Data often reveals inequities and disparities between different populations. These can be influenced further by factors such as economic differences and racial biases. Historically, the Black community has experienced worsened outcomes in the healthcare system due to a large combination of factors—factors we may be able to confront through better technology and better data.

Machine learning is one of the technologies helping to reveal patterns in data sets that may be otherwise invisible due to preexisting biases. We can mine health industry information to identify and address problems through big data. It’s essential for healthcare providers, institutions, and even individuals within the industry to understand and acknowledge the disparities within these processes so they can better provide care to the Black community.

This week we take a look at the long-standing neglect of African-American communities and the vulnerable position they are in to experience the pandemic at a greater scale. There is an opportunity to apply machine learning (ML) to identify social and health determinants of health disparities to ascertain why they exist and strategies to improve health care.

Continue reading, “DeepTrack: An ML-based Approach to Health Disparity Identification and Determinant Tracking for Improving Pandemic Health Care” by Jinwei Liu (Florida A&M University), Long Cheng (North China Electric Power University), Ankur Sarker (Univesity California Los Angeles), Li Yan (Xi’an Jiaotong University) Richard A. Alo (Florida A&M University)


The Coronavirus disease 2019 (COVID-19) pandemic has severely impacted countries around the world with unprecedented mortality and economic devastation and has disproportionately and negatively impacted different communities—especially racial and ethnic minorities who are at a particular disadvantage. Black Americans have a long-standing history of disadvantage (e.g., long-standing disparities in health outcomes) and are in a vulnerable position to experience the impact of this pandemic. Some studies indicate high-risk and vulnerability of the elderly and patients with underlying co-morbidities, however, little research paid attention to leveraging geographic information and machine learning (ML) to track the social and structural health determinants, which can provide a lower level of granularity. In this paper, we propose DeepTrack, a geospatial and ML-based approach to identify diverse determinants (including the structural, social, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities and diets based on multiple COVID-19 datasets and examine the structural, social, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in infection and death rates due to COVID-19 pandemic. We track determinants of nutrition and obesity through diet examination. Extensive experimental results show the effectiveness of our approach. The research provides new strategies for health disparity identification and determinant tracking with a goal to improve pandemic health care.