Kennesaw State researcher earns NIH grant to investigate health factors related to obesity using AI

Kennesaw State University researcher Liang Zhao sits a a desk, surrounded by papers and a laptop computer

Photo above by Matt Yung

[This article by Raynard Churchwell first appeared on the Kennesaw State University website, republished with permission]

The Centers for Disease Control and Prevention (CDC) recently reported that obesity is prevalent in more than 40 percent of U.S. adults. To combat this, Kennesaw State University assistant professor Liang Zhao aims to use artificial intelligence (AI) and machine learning to explore health determinants that lead to obesity in rural areas.  

Zhao’s research, supported by a $51,747 National Institutes of Health (NIH) AIM-AHEAD grant, examines the social determinants of health, or SDOH. These determinants account for up to 70 percent of health outcomes and are the significant drivers of health disparities. Social determinants account for factors like geographic location such as living in rural communities. Studies show obesity rates are approximately 6.2 times higher in rural areas than in urban areas in the U.S., which can significantly impact health outcomes. The research also explores how these factors relate to the widespread presence of obesity in rural communities. 

“These figures show the urgent need for targeted results in rural areas and highlight necessary changes,” said Zhao, who teaches in Kennesaw State’s College of Computing and Software Engineering.  

Rural residents often face significant disadvantages, including limited healthcare resources. To address obesity disparities, Zhao aims to improve understanding of social determinants of health (SDOH) by leveraging artificial intelligence and machine learning on large datasets from community health records.

One key resource is the OCHIN database, a nonprofit health care innovation center that aggregates electronic health record and SDOH data for over six million patients across 33 states. Through this research, Zhao hopes to uncover evidence that informs policy reforms. The outcomes will focus on the associations between SDOH and obesity, providing recommendations for government officials to implement changes in policy, structure, and infrastructure in rural communities.

“We want to build a model that identifies contributing factors specific to these populations to drive widespread change,” he said.  

Shaoen Wu, chair of KSU’s Department of Information Technology, praised Zhao’s efforts. 

“We are proud to support professor Zhao and his important research,” Wu said. “His work exemplifies our commitment to addressing real-world challenges and improving health outcomes in our communities.” 

A key objective of Zhao’s research is to create a predictive model pinpointing factors leading to higher obesity rates. This is vital for informing policies and improving rural healthcare services, which often lack the resources for comprehensive data analysis.  

Rural facilities frequently struggle with insufficient electronic health records and staff shortages, negatively impacting patient care. Zhao’s research seeks to leverage AI to automate processes, increasing efficiency in health delivery. 

Collaboration is crucial to the project. Zhao is partnering with the AIM Ahead program, the Morehouse School of Medicine, and the Harvard Medical School; gaining access to experts and community stakeholders.  

“Working with other brilliant minds allows us to create innovative solutions for the communities that need them most,” he said. 

To assess his research model’s effectiveness, Zhao plans to compare findings with real-world data from rural hospitals, aiming to develop a comprehensive AI-enabled framework that refines data with SDOH. The approach ensures that solutions are effective in addressing local health issues. 

Community engagement is another critical aspect. Zhao aims to share findings through workshops and meetings, fostering discussions among residents and healthcare providers about health outcomes and potential solutions.  

Looking beyond the current project, Zhao envisions a comprehensive network for rural healthcare that incorporates community input while maintaining patient privacy.  

“If successful, this research could open new doors for funding and initiatives to address more health disparities,” Zhao said. 

Story by Raynard Churchwell

Photos by Matt Yung