{"693393":{"#nid":"693393","#data":{"type":"news","title":"AI Improves Diabetes Care One Decision at a Time","body":[{"value":"\u003Cp\u003EPublic health systems often face a difficult challenge when time, funding, and resources are limited: who should receive care first? These decisions are complicated further by differences in patient needs, disease progression, and social factors like access to care.\u003C\/p\u003E\u003Cp\u003EA team of computer scientists and doctors led by Georgia Tech aims to change that. Using a system of artificial intelligence (AI) models, the team can help clinicians deliver the right intervention to the right patient at the right time.\u003C\/p\u003E\u003Cp\u003ETheir system uses Restless Multi-Armed Bandit (RMAB) models. While the name is technical, the idea is simple. The RMAB framework helps decision-makers choose the best actions over time when conditions are complex and constantly changing.\u003C\/p\u003E\u003Cp\u003EThis project will tackle Type 2 Diabetes (T2D), building on previous experience expanding RMAB models to support maternal health care in India. Success on this front could lead to treating other chronic illnesses, like hypertension, heart disease, kidney disease, and mental health.\u003C\/p\u003E\u003Cp\u003ERecognizing the RMAB system\u2019s potential, the National Institutes of Health (NIH)\u0026nbsp; awarded the group an R01 grant.\u0026nbsp;\u003Ca href=\u0022mailto:https:\/\/reporter.nih.gov\/search\/Dar1PemdxE2k-lko4bhfMQ\/project-details\/11418002\u0022\u003EThe award will fund the project\u003C\/a\u003E with $1.2 million through 2029.\u003C\/p\u003E\u003Cp\u003E\u201cOur goal is to address the immense challenge of allocating limited health intervention resources in optimal ways,\u201d said\u0026nbsp;\u003Ca href=\u0022mailto:https:\/\/guaguakai.com\/\u0022\u003EKai Wang\u003C\/a\u003E, an assistant professor in the School of Computational Science and Engineering (\u003Ca href=\u0022mailto:https:\/\/cse.gatech.edu\/\u0022\u003ECSE\u003C\/a\u003E).\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cBy leveraging rich contextual data and accounting for social determinants, we envision RMAB models that empower health centers to customize interventions to individual patient profiles. Ultimately, this advances precision medicine and ensures equitable healthcare access for diverse populations.\u201d\u003C\/p\u003E\u003Cp\u003ET2D is a diabetes condition where the body resists insulin or doesn\u2019t produce enough to regulate blood sugar levels. Left unmanaged, high blood sugar leads to severe health problems like heart disease and stroke, damage to organs like eyes and kidneys, nerve damage, infections, and ulcers.\u003C\/p\u003E\u003Cp\u003ET2D is challenging to treat because patients have different ages, genetics, lifestyles, and body composition, all of which affect the progress of the disease. Social factors, like transportation and family support, matter just as much as biology to treat the disease.\u003C\/p\u003E\u003Cp\u003EFor these reasons, patients who get the same medications may get very different results. While treatment plans now incorporate new GLP-1 drugs, they remain expensive and difficult to distribute equitably.\u003C\/p\u003E\u003Cp\u003E\u201cDiabetes is a great example of a complicated, multi-faceted problem. It is a natural playground for the kind of high-dimensional machine learning algorithms we want to build and deploy,\u201d said School of CSE Professor\u0026nbsp;\u003Ca href=\u0022mailto:https:\/\/faculty.cc.gatech.edu\/~badityap\/\u0022\u003EB. Aditya Prakash\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003E\u201cAs a chronic disease, diabetes also creates opportunities for sequential decision-making. Providers are not making just one decision to treat the condition, but rather a series of decisions over time.\u201d\u003C\/p\u003E\u003Cp\u003ETrue to its multi-armed name, the team\u2019s framework applies RMAB models across three different thrusts.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EContextual RMABs use patient histories, biomarkers, demographics, and other data to predict how each individual will respond to an intervention. These models directly provide personalized, data-driven treatments.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EBy incorporating patient-specific information, contextual RMAB models can help healthcare providers prioritize the most timely interventions. The goal is to improve patient outcomes while making more efficient use of limited healthcare resources.\u003C\/p\u003E\u003Cp\u003ENext, the group wants to understand social life and community influence in public health. Families and social networks shape how people manage chronic health conditions.\u003C\/p\u003E\u003Cp\u003ENetwork RMABs study and account for the role of social factors in health interventions. For example, the models can recognize when one patient responds better to treatment than others through benefits of their social circle.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EMore broadly, hospitals, clinics, government agencies, and nonprofit organizations often share responsibility for the same population in an area. Sometimes, they even compete for the same medications or funding.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EMulti-agent RMABs are the third glass of models aimed at decentralized health systems to make fairer decisions. These tools examine how to coordinate resources and decisions between stakeholders. This in turn leads to policies that encourage cooperation and equitable resource distribution.\u003C\/p\u003E\u003Cp\u003E\u201cI feel extremely confident in RMABs because we have already seen these frameworks deliver real-world impact,\u201d Wang said.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cPrior success has proven the value of data-driven decision-making under resource constraints, giving us a strong blueprint to effectively address the nuances and complexities of diabetes.\u201d\u003C\/p\u003E\u003Cp\u003EWang previously built RMAB models to expand maternal healthcare for women in India. Working in conjunction with the non-profit organization ARMMAN, the models used healthcare information to identify and reach the most vulnerable populations.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EBy integrating RMAB models into ARMMAN mobile health technology, the partnership has reached over 350,000\u0026nbsp;mothers. This further led to a 31% improvement in engagement with health information.\u003C\/p\u003E\u003Cp\u003ECompared to maternal healthcare, diabetes is a more nuanced disease with differing causes, symptoms, and treatments. This requires a team effort spanning institutions and backgrounds. \u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. \u003Cstrong\u003ESara Cromer\u003C\/strong\u003E\u0026nbsp;is an endocrinologist at Massachusetts General Hospital (MGH). Experienced diagnosing and treating diabetes, she has studied how social issues and equity influences the disease. MGH will provide much of the patient data to train the RMAB models.\u003C\/p\u003E\u003Cp\u003EAssociate Professor\u0026nbsp;\u003Ca href=\u0022mailto:https:\/\/sites.google.com\/stonybrook.edu\/jianli\u0022\u003EJian Li\u003C\/a\u003E studies reinforcement learning at Stony Brook University. His expertise in applying reinforcement learning for resource allocation plays a key part tailoring models for the T2D problem. Wang and Li have previously worked together on RMAB research.\u003C\/p\u003E\u003Cp\u003EPrakash is an expert in computational epidemiology at Georgia Tech. He has led substantial contributions to the use of AI for networks to predict outbreaks and track disease spread. This experience has led to collaborations with NIH, the Centers for Disease Control and Prevention (CDC), National Science Foundation, and others.\u003C\/p\u003E\u003Cp\u003E\u201cThis project speaks to the interdisciplinary problem-solving we try to achieve in CSE. Despite having different backgrounds, our interest in applying our strengths to real-world healthcare problems unites us together,\u201d Prakash said.\u003C\/p\u003E\u003Cp\u003E\u201cThese problems require experts from all sides. Without computational scientists, we can\u2019t take advantage of available data. We lean on clinical experts to provide the data and expertise needed to guide our methods and make sense of results. Ultimately, these approaches need to be clinically validated if we want to make real-world progress.\u201d\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EPublic health systems often face a difficult challenge when time, funding, and resources are limited: who should receive care first? These decisions are complicated further by differences in patient needs, disease progression, and social factors like access to care.\u003C\/p\u003E\u003Cp\u003EA team of computer scientists and doctors led by Georgia Tech aims to change that. Using a system of artificial intelligence (AI) models, the team can help clinicians deliver the right intervention to the right patient at the right time.\u003C\/p\u003E\u003Cp\u003ETheir system uses Restless Multi-Armed Bandit (RMAB) models. While the name is technical, the idea is simple. The RMAB framework helps decision-makers choose the best actions over time when conditions are complex and constantly changing.\u003C\/p\u003E\u003Cp\u003EThis project will tackle Type 2 Diabetes (T2D), building on previous experience expanding RMAB models to support maternal health care in India. Success on this front could lead to treating other chronic illnesses, like hypertension, heart disease, kidney disease, and mental health.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"School of CSE researchers Kai Wang and B. Aditya Prakash are PIs of a $1.2 million NIH R01 grant to build AI models that consider individual, social, and system factors when treating patients with Type 2 Diabetes and other chronic illnesses."}],"uid":"36319","created_gmt":"2026-10-22 13:51:13","changed_gmt":"2026-10-26 12:55:43","author":"Bryant Wine","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2026-10-22T00:00:00-04:00","iso_date":"2026-10-22T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"681512":{"id":"681512","type":"image","title":"Wang-Prakash-NIH-R01-Head-Image.jpg","body":null,"created":"1792677082","gmt_created":"2026-10-22 13:51:22","changed":"1792677082","gmt_changed":"2026-10-22 13:51:22","alt":"CSE Wang Prakash NIH R01 Grant","file":{"fid":"265923","name":"Wang-Prakash-NIH-R01-Head-Image.jpg","image_path":"\/sites\/default\/files\/2026\/10\/22\/Wang-Prakash-NIH-R01-Head-Image.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/10\/22\/Wang-Prakash-NIH-R01-Head-Image.jpg","mime":"image\/jpeg","size":174520,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/10\/22\/Wang-Prakash-NIH-R01-Head-Image.jpg?itok=F247Tcxl"}},"681513":{"id":"681513","type":"image","title":"RMAB-Figure-3.png","body":null,"created":"1792677124","gmt_created":"2026-10-22 13:52:04","changed":"1792677124","gmt_changed":"2026-10-22 13:52:04","alt":"CSE Wang Prakash NIH R01 Grant","file":{"fid":"265924","name":"RMAB-Figure-3.png","image_path":"\/sites\/default\/files\/2026\/10\/22\/RMAB-Figure-3.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/10\/22\/RMAB-Figure-3.png","mime":"image\/png","size":82207,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/10\/22\/RMAB-Figure-3.png?itok=DiMd3WJi"}}},"media_ids":["681512","681513"],"groups":[{"id":"47223","name":"College of Computing"},{"id":"1188","name":"Research Horizons"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[{"id":"194606","name":"Artificial Intelligence"},{"id":"138","name":"Biotechnology, Health, Bioengineering, Genetics"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"146","name":"Life Sciences and Biology"},{"id":"135","name":"Research"}],"keywords":[{"id":"654","name":"College of Computing"},{"id":"166983","name":"School of Computational Science and Engineering"},{"id":"187915","name":"go-researchnews"},{"id":"9153","name":"Research Horizons"},{"id":"10199","name":"Daily Digest"},{"id":"181991","name":"Georgia Tech News Center"},{"id":"170447","name":"Institute for Data Engineering and Science"},{"id":"176858","name":"machine learning center"},{"id":"9167","name":"machine learning"},{"id":"187812","name":"artificial intelligence (AI)"},{"id":"192863","name":"go-ai"},{"id":"194384","name":"Tech AI"},{"id":"49591","name":"Diabetes"},{"id":"186687","name":"diabetes research"},{"id":"755","name":"public health"},{"id":"108101","name":"chronic disease"}],"core_research_areas":[{"id":"193655","name":"Artificial Intelligence at Georgia Tech"},{"id":"39441","name":"Bioengineering and Bioscience"},{"id":"39431","name":"Data Engineering and Science"}],"news_room_topics":[{"id":"71891","name":"Health and Medicine"}],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EBryant Wine, Communications Officer\u003Cbr\u003E\u003Ca href=\u0022mailto:bryant.wine@cc.gatech.edu\u0022\u003Ebryant.wine@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}