Spatial Health AI Research Partnership
The Spatial Health AI Research Partnership (SHARP) is a global research and training network dedicated to advancing health and environmental resilience through the integration of geospatial science, artificial intelligence, and data-driven policy analysis. SHARP harnesses the power of interdisciplinary collaboration to address complex and urgent challenges in public health, climate change, sustainability planning and disaster risk management. By fostering partnerships with academic institutions, industry leaders, and professional communities worldwide, SHARP seeks to generate innovative solutions, inform evidence-based decision-making, and train the next generation of scholars and practitioners at the intersection of health policy, spatial analysis and AI.
News and events
Researchers Explore How AI Tracks Breathing, Predicts Air Quality
September 2025
In two recent studies, University of Texas at Dallas researchers demonstrated how artificial intelligence (AI) and machine learning can be used to address a variety of issues from a social science policy perspective. Dr. Dohyeong Kim, a researcher in the School of Economic, Political and Policy Sciences (EPPS), and collaborators in South Korea have developed a wearable stethoscope that uses AI to monitor a patient’s breathing sounds for wheezing. Kim is also part of a team using machine learning to predict levels of airborne bacteria and fungi in indoor environments.
“In EPPS, we have multiple scholars working on AI issues,” said Kim, a professor of public policy, geospatial information sciences (GIS), and social data analytics and research, and senior associate dean of graduate education for EPPS. “AI applications have been primarily the domain of computer scientists or engineers, but it is getting more important to understand how AI can be applied in social science, health care, education, the environment and other areas.”
Kim and his colleagues previously developed a novel AI-based method for counting wheezing events in patients that can indicate breathing trouble that needs medical attention. The wearable stethoscope, described in a new article in the journal Engineering, is a wireless, skin-attachable, low-power device that includes a lung-sound monitoring patch (LSMP). The LSMP monitors respiratory function through a mobile app and classifies normal and problematic breathing by comparing their unique acoustic characteristics. In the study, which included corresponding authors from South Korea, the LSMP sensor was tested in pediatric patients with asthma and elderly patients with chronic obstructive pulmonary disease (COPD). The AI-based breathing-event counter was able to distinguish more than 80% of abnormal events, especially wheezing, in the COPD patients.
“In the previous study, we developed a method of training the algorithm with the wheezing sounds, but at the time we had not fully developed the wearable devices,” Kim said. “With the stethoscope fully developed, we can use this AI algorithm to automatically detect in real time whether the breathing sounds are normal. We can monitor and see the intensity and frequency of those wheezing sounds.”
In a related study, published in the Feb. 15 issue of the journal Building and Environment, researchers used machine learning to examine the combined effect of temperature and humidity on indoor bioaerosol concentrations. Exposure to airborne bioaerosols, such as bacteria and fungi, presents significant health risks, especially for vulnerable populations like children, the elderly and those with compromised immune systems. Bioaerosol exposure can aggravate respiratory and allergic conditions, underscoring the need for real-time monitoring in indoor environments. The researchers analyzed data collected from 4,048 samples across 10 types of multiuse facilities, including day care centers and libraries in South Korea, and showed that temperature and humidity jointly and significantly affected concentrations of bacteria and mold. Kim said the findings provide guidelines for controlling indoor bioaerosol levels and creating safer and healthier indoor environments by adjusting temperature and humidity.
In addition to Kim, Gloria Geevarghese BS’24 is an author of the Building and Environment study, along with researchers from Yonsei University, Seokyeong University and Korea University. Additional authors of the Engineering study included researchers from the Korea Institute of Science and Technology, Ajou University, Kosin University College of Medicine and Seokyeong University. Crowdsourced Data Could Help Map Urban Food Deserts.
Dr. Dohyeong Kim got awarded the Award of Appreciation by the Asia Disaster Preparedness Center (ADPC)
July 2024
Dean Jennifer Holmes and Associate Dean Dohyeong Kim participated in the 18th International Conference on Crisis and Emergency Management, held in Osong, Chungbuk Province, South Korea, from July 12-14. The conference focused on the need for global collaboration to improve disaster resilience in response to the climate crisis. Both Dean Holmes and Dr. Kim delivered keynote presentations at the conference. Dean Holmes presented on the topic of protecting first responders at the scene of terrorist attacks. Dr. Kim discussed his research on identifying asthma-prone children exposed to particulate matter using deep learning, outlining a path toward personalized risk prediction and intervention. Dr. Kim was awarded the Award of Appreciation by the Asia Disaster Preparedness Center (ADPC), an autonomous international organization established in 1986 in Bangkok, Thailand, in recognition of his scholarly contributions to the field of disaster policy and management.