Health and Wellness

computational health and medicine

Analyzing data to unlock improved outcomes

Theodora Bourni, associate professor of mathematics, and two PhD students (Layal Bou Hamdan and Alireza Shahi) discuss their research using artificial intelligence to improve cleft lip surgeries inside an Ayres Hall classroom on the University of Tennessee campus.
College of Nursing students work on their maternal/child skills inside the HITS lab at the University of Tennessee, Knoxville.
College of Nursing students work on their maternal/child skills
Alexis McCraw, 3rd year Phd student in Experimental Psychology, uses computer games to stimulate a minor’s brain waves in order to gain a better understanding of different aspects of child development.

UT faculty integrate advanced mathematical modeling and machine learning with health, genetics, and genomics expertise. By quantifying large multimodal data sets, they uncover patterns and interactions between health outcomes, behaviors, and environmental conditions.

Their insights enable clinicians and public health leaders to better predict, diagnose, treat, and manage chronic diseases and acute conditions. In collaboration with health care providers, UT researchers are leading the way toward transformative patient-tailored interventions that save lives and enhance outcomes across the continuum of care.

Detail macro photo of a LTO tape machine working at the High Performance Scientific Computing’s ISAAC (Infrastructure for Scientific Applications and Advanced Computing) data center at the University of Tennessee, Knoxville.

UT’s Approach

Researchers leverage electronic health records, electrocardiogram data, and other medical records to develop tools to predict and enable earlier detection of acute conditions like preeclampsia and acute kidney injury. UT researchers developed an award-winning AI model to predict sepsis development & enable effective treatment.

The same resources are helping researchers automate diagnostics and optimize treatments for chronic conditions. UT innovations include an advanced analytical model to automate atrial fibrillation identification using single-lead ECGs like those in wearable technologies.

Collaborations with clinicians at UT Medical Center are moving research to solutions in anesthesiology, maternal and fetal health, and cancer diagnosis. One team is developing cost-effective AI and machine learning models for rapid, reliable breast cancer staging based on pathology reports.

Researchers frequently apply machine learning to reveal complex interactions between health outcomes, genomics, and external factors Faculty working in areas spanning health and environment are building a statewide research community focused on the links between widespread health issues, environmental factors like pollution and water quality, and social determinants of health.

“We leverage data from partners located from one end of Tennessee to the other. This gives us opportunities to undertake research and build models that broaden our perspectives on health care challenges affecting different populations in both urban and rural areas.”

— Anahita Khojandi, Heath Endowed Faculty Fellow in Business and Engineering and Professor of Industrial and Systems Engineering

Kayla, a College of Nursing student, uses a pen light to check the eyes of a simulated patient under the supervision of Sadie Thompson, a clinical instructor, while working in a lab in the nursing building at Research Park.
Alexis McCraw, 3rd year Phd student in Experimental Psychology, and Sydney Thompson, undergrad neuroscience major, discuss eye movements and brain waves of a minor in order to gain a better understanding of different aspects of child development in the Attention, Brain, and Cognition Lab in the Austin Peay Building on the University of Tennessee campus.
A minor smiles as Sydney Thompson, undergrad neuroscience major, adjust her head gear before a observation to gain a better understanding of different aspects of child development in the Attention, Brain, and Cognition Lab in the Austin Peay Building on the University of Tennessee campus.
Anna Collins, a College of Nursing student, talks with April Bryant, clinical faculty, in a lab inside the nursing building in Research Park.
College of Nursing instructors Audra Allen, a clinical assistant professor, and Sadie Thompson, a clinical instructor, observe nursing students, from a control room, working in a simulation lab inside the nursing building at Research Park

Highlights

Xueping Li, Professor and Dan Doulet Faculty Fellow in Industrial and Systems Engineering; and Bing Yao, Dan Doulet Early Career Assistant Professor, Industrial and Systems Engineering post outside the Tickle Engineering Building on the University of Tennessee campus.

Collaboration Between UT and UTMC Could Revolutionize Breast Cancer Diagnosis

UT researchers are using AI and machine learning to help doctors provide treatment plans to breast cancer patients more quickly by analyzing pathology reports and other clinical records to determine how much breast cancer is in the body.

Learn about this game-changing research.

Photo of a researcher filling a test tube with a liquid in a lab at the University of Tennessee, Knoxville.

Transdisciplinary Team Receives Collaborative Grant to Improve Disease Management

With support from a new collaborative grant program, this team is using machine learning–based processes to improve disease management. Their work will bring together translational research, new technologies, and health information.

Learn about this UT team and other grant recipients.

Photo of the Torchbearer statue on the campus of the University of Tennessee, Knoxville.

UT, UTHSC Researchers Apply Data Expertise to Advance Knowledge of Kidney Disease

Michael A. Langston, a professor of electrical engineering and computer science, is applying his expertise in data science and machine learning to associations between risk factors, exposures, and clinical outcomes for patients at various stages of chronic kidney disease.

Learn more about Langston’s research.

Bing Yao, Dan Doulet Early Career Assistant Professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville.

Yao Receives $1.1 Million to Create Algorithm that Personalizes Heart Surgery

Bing Yao aims to optimize cardiac surgical planning by creating an AI algorithm that understands atrial fibrillation in unprecedented detail. Her team received a four-year grant from a joint National Science Foundation–National Institutes of Health program.

Read more about this project. 

A nursing student at the University of Tennessee, Knoxville, checks on a simulated patient while working in a lab.

Facilities & Initiatives

Our research takes place in state-of-the-art facilities across the state, from the Center for Precision Health in Knoxville to the Center for Biomedical Informatics housed in Memphis.

Visual Learning lab during the Center for Precision Health dedication.

Researchers

  • Rigoberto Advincula

    Rigoberto Advincula

    UT-ORNL Governor’s Chair for Advanced & Nanostructured Materials

    Synthesis of biomaterials, biomedical devices, drug delivery, biomedical engineering, biosensors for health monitoring, applications of artificial intelligence, machine learning (ML) and deep learning (DL) in various science and engineering domains in: biopolymers, nanoscience, drug development, theranostic agents, developing new biomedically relevant instrumentations and sensor/monitoring environments for in vivo and in vitro methods

  • Tom Berg

    Tom Berg

    Director of the Applied Systems Lab; Assistant Professor, Nursing; Assistant Professor, Engineering

    Machine learning, hybrid models-based systems engineering, digital twin design for high consequence environments, decision support systems

  • Matt Harris

    Matt Harris

    Boyd Distinguished Professor of Health Economics

    Labor economics, health economics, access of care, affordability of care, dynamic discrete choice models, opioids, ACEs, educational outcomes

  • Anahita Khojandi.

    Anahita Khojandi

    Heath Endowed Faculty Fellow in Business & Engineering and Associate Professor, Industrial and Systems Engineering

    Markov decision processes, dynamic programming, predictive analytics, reinforcement learning, time series analysis, anomaly detection, genomics, critical care, chronic care, emergency medicine

  • Vasileios Maroulas

    Vasileios Maroulas

    Professor, Mathematics, Associate Vice Chancellor, Director, AI Tennessee

    Artificial intelligence, data science, quantum computing, machine learning, computational statistics, Bayesian statistics, topological data analysis, uncertainty quantification

  • Phoebe Tran

    Phoebe Tran

    Assistant Professor, Public Health

    Chronic disease prevention, health disparities, medically underserved populations, AI and machine learning prediction models, smart technology

  • Bing Yao

    Bing Yao

    Dan Doulet Early Career Assistant Professor, Industrial and Systems Engineering

    Big data analytics, biomedical and health informatics, physics-informed machine learning, computer simulation and optimization, data mining and signal processing, sequential decision making, sensor-based modeling and control

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