{"id":348,"date":"2023-01-13T12:31:03","date_gmt":"2023-01-13T17:31:03","guid":{"rendered":"https:\/\/research-dev.utk.edu\/humanhealth\/?page_id=348"},"modified":"2026-05-20T08:29:33","modified_gmt":"2026-05-20T12:29:33","slug":"computational-health-and-medicine","status":"publish","type":"page","link":"https:\/\/research.utk.edu\/health-wellness\/research-areas\/computational-health-and-medicine\/","title":{"rendered":"Computational Health and Medicine"},"content":{"rendered":"\n
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computational health and medicine<\/h1>\n\n\n\n

Analyzing data to unlock improved outcomes<\/em> <\/em><\/p>\n<\/div>

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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.<\/p>\n\n\n\n

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.<\/p>\n<\/div>\n\n\n\n

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UT’s Approach<\/h2>\n\n\n\n

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<\/a> & enable effective treatment.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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

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\u201cWe 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.\u201d<\/p>\n\n\n\n

\u2014 Anahita Khojandi, Heath Endowed Faculty Fellow in Business and Engineering and Professor of Industrial and Systems Engineering<\/em><\/p>\n<\/div><\/div>\n\n\n\n

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Highlights<\/h2>\n\n\n\n
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Collaboration Between UT and UTMC Could Revolutionize Breast Cancer Diagnosis<\/a><\/h3>\n\n\n\n

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.<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Transdisciplinary Team Receives Collaborative Grant to Improve Disease Management<\/a><\/h3>\n\n\n\n

With support from a new collaborative grant program, this team is using machine learning\u2013based 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.<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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UT, UTHSC Researchers Apply Data Expertise to Advance Knowledge of Kidney Disease<\/a><\/h3>\n\n\n\n

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.

<\/a>Learn more about Langston\u2019s research.<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Yao Receives $1.1 Million to Create Algorithm that Personalizes Heart Surgery<\/a><\/h3>\n\n\n\n

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\u2013National Institutes of Health program.

<\/a><\/a>Read more about this project. <\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Facilities & Initiatives<\/h2>\n\n\n\n
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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.<\/p>\n\n\n\n