{"id":57,"date":"2024-01-31T16:29:30","date_gmt":"2024-01-31T16:29:30","guid":{"rendered":"https:\/\/tickle.utk.edu\/ise\/?p=57"},"modified":"2024-12-10T17:28:24","modified_gmt":"2024-12-10T17:28:24","slug":"ut-team-uses-ai-to-improve-sepsis-detection-and-effective-treatment","status":"publish","type":"post","link":"https:\/\/tickle.utk.edu\/ise\/ut-team-uses-ai-to-improve-sepsis-detection-and-effective-treatment\/","title":{"rendered":"UT Team Uses AI to Improve Sepsis Detection and Effective Treatment"},"content":{"rendered":"

Sepsis acquired in clinical settings threatens the lives of tens of millions of people worldwide every year. The condition, in which the body responds to an infection by essentially going into overdrive, inadvertently attacks the body by overzealously releasing chemicals to defend it.<\/p>\n

A\u00a0World Health Organization study<\/a>\u00a0found that more than 44 million people had sepsis in 2017, causing 11 million sepsis-related deaths and leading to other significant adverse events such as limb amputations.<\/p>\n

Sepsis is treatable if caught in time, but many patients show no signs of the condition until it\u2019s too late in the cycle to cure effectively and expeditiously.<\/p>\n

\u201cYou have to catch it early,\u201d said\u00a0Anahita Khojandi<\/a>, Heath Endowed Faculty Fellow in Business and Engineering and associate professor in the\u00a0Department of Industrial and Systems Engineering\u00a0at the University of Tennessee, Knoxville. \u201cWith our new predictive tools, we think health care professionals will be able to catch it at least four to six hours sooner, leading to more effective treatment and better health outcomes. That might not sound like a lot of time, but sepsis moves so rapidly that four to six hours could be the difference between life and death.\u201d<\/p>\n

Khojandi and a multidisciplinary team of researchers from UT hypothesized that by building out datasets developed from electronic health records and analyzing them for patterns among patients who later developed sepsis, they would be able to predict the onset of the condition. An ISE colleague\u2014Xueping Li<\/a>, Dan Doulet Faculty Fellow and professor\u2014was able to connect the team to an initial set of data owners to get them started.<\/p>\n

\u201cHe had a colleague in the\u00a0Center for Health Systems Innovation<\/a>\u00a0at Oklahoma State University who had access to electronic health records data that we needed to start on this journey of building models for early sepsis prediction,\u201d Khojandi said. \u201cFrom there, we started learning more about the power of electronic health records data and their potential shortcomings. We were able to access additional datasets and to perform a series of studies, each building on the previous one, until we could finally address the problem in a holistic way.\u201d<\/p>\n

One of the key initial shortcomings was the lack of granularity in some of the electronic health records. Fortunately, UT\u2013Oak Ridge National Laboratory Governor\u2019s Chair for Biomedical Informatics\u00a0Robert Davis<\/a>\u00a0in the\u00a0UT Health Science Center<\/a>\u00a0was able to help the team address this key challenge by providing Khojandi with a novel dataset of patients\u2019 vital readings, such as heart rate and respiratory rate, collected continuously from ICU monitors. The new dataset allowed the team to develop a powerful AI framework by leveraging time series\u2013based modeling and sequential decision-making approaches. In their latest work, they developed a novel approach that allows the AI model to peek into the hidden health state of a patient in real time while accounting for the patient\u2019s underlying disease progression. The model can then construct an accurate picture of the patient\u2019s state, improving the decision-making process.<\/p>\n

This recent study from the Khojandi team received the prestigious\u00a0Harvey J. Greenberg Research Award<\/a>\u00a0from the INFORMS Computing Society, which honors research excellence in the field of computation and operations research applications, especially those in emerging application fields.<\/p>\n

Khojandi said collaboration is extremely important to her success, since hard problems often require multidisciplinary expertise and methods. She added that researchers from engineering, mathematics, and health care\u2014including medical doctors and nurses, social workers, and other professionals\u2014need to work together to make sure they look at problems holistically in order to develop impactful solutions.<\/p>\n

Future Directions for AI<\/h2>\n

The sepsis project demonstrates the potential power of AI in medical applications. Khojandi is now teaming up with new collaborators to apply her expertise and tools to solve different problems.<\/p>\n

She is working with\u00a0Vasileios Maroulas<\/a>, assistant vice chancellor, deputy director of the\u00a0AI Tennessee Initiative<\/a>, and a professor of mathematics, and\u00a0Scott Emrich<\/a>, associate professor of computer science. They are leveraging data in electronic health records, including physiological and imaging data, to develop models that help with treatment planning.<\/p>\n

For example, Maroulas and Khojandi are working with\u00a0Jason Buehler<\/a>\u00a0and\u00a0Patrick McFarland<\/a>\u00a0in the Department of Anesthesiology at the\u00a0UT Medical Center<\/a>\u00a0to develop AI models that can predict opioid-induced ventilatory insufficiency in patients in hospital settings. They are also collaborating with Stefanos Boukovalas and Devin Clegg from the Department of Surgery at UTMC to predict the risk of lymphedema following breast cancer and identify additional factors to help guide treatment planning. Emrich and Khojandi are working with\u00a0Jill Maples<\/a>,\u00a0Kimberly Fortner<\/a>,\u00a0Nikki Zite<\/a>, and\u00a0Callie Reeder<\/a>\u2014all from the Department of Obstetrics and Gynecology at UTMC\u2014to use AI to improve maternal and fetal care. In addition to their positions at UTMC, Buehler, Boukovalas, Clegg, Maples, Zite, and Reeder are also faculty members of\u00a0UT Health Science Center\u2019s College of Medicine<\/a>.<\/p>\n

\u201cOverall, I sincerely believe that our models can revolutionize health and health care and provide great support to health professionals caring for patients at the bedside; however, care must be taken when developing the models to make sure they are equitable, unbiased, and practical and their results remain reproducible,\u201d Khojandi said. \u201cSo we have a long road ahead of us, but I\u2019m optimistic and excited about what the future holds.\u201d<\/p>\n

While the study is just the first step, it has established a foundation for future successes\u2014all made possible by the spirit of collaboration and a commitment to ensuring that UT research is making life and lives better.<\/p>\n","protected":false},"excerpt":{"rendered":"

ISE Associate Professor Anahita Khojandi and a multidisciplinary team of researchers from UT use data and predictive tools to improve the detection of sepsis.<\/p>\n","protected":false},"author":32,"featured_media":58,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[4],"tags":[9,10,11,12],"class_list":["post-57","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research","tag-anahita-khojandi","tag-artificial-intelligence","tag-utmc","tag-xueping-li"],"yoast_head":"\nUT Team Uses AI to Improve Sepsis Detection and Effective Treatment - Industrial and Systems Engineering<\/title>\n<meta name=\"description\" content=\"ISE Associate Professor Anahita Khojandi and a multidisciplinary team of researchers from UT use data and predictive tools to improve the detection of sepsis.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/tickle.utk.edu\/ise\/ut-team-uses-ai-to-improve-sepsis-detection-and-effective-treatment\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"UT Team Uses AI to Improve Sepsis Detection and Effective Treatment - 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