A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph
Abstract Pediatric diabetes I is an endemic and an especially difficult disease; indeed, at this point, there does not exist a cure, but only careful management that relies on anticipating hypoglycemia. The changing physiology of children producing unique blood glucose signatures, coupled with incon...
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| Format: | Article |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-82649-4 |
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| author | Mert Onur Cakiroglu Hasan Kurban Lilia Aljihmani Khalid Qaraqe Goran Petrovski Mehmet M. Dalkilic |
| author_facet | Mert Onur Cakiroglu Hasan Kurban Lilia Aljihmani Khalid Qaraqe Goran Petrovski Mehmet M. Dalkilic |
| author_sort | Mert Onur Cakiroglu |
| collection | DOAJ |
| description | Abstract Pediatric diabetes I is an endemic and an especially difficult disease; indeed, at this point, there does not exist a cure, but only careful management that relies on anticipating hypoglycemia. The changing physiology of children producing unique blood glucose signatures, coupled with inconsistent activities, e.g., playing, eating, napping, makes “forecasting” elusive. While work has been done for adult diabetes I, this does not successfully translate for children. In the work presented here, we adopt a reinforcement approach by leveraging the de Bruijn graph that has had success in detecting patterns in sequences of symbols–most notably, genomics and proteomics. We translate a continuous signal of blood glucose levels into an alphabet that then can be used to build a de Bruijn, with some extensions, to determine blood glucose states. The graph allows us to “tune” its efficacy by computationally ignoring edges that provide either no information or are not related to entering a hypoglycemic episode. We can then use paths in the graph to anticipate hypoglycemia in advance of about 30 minutes sufficient for a clinical setting and additionally find actionable rules that accurate and effective. All the code developed for this study can be found at: https://github.com/KurbanIntelligenceLab/dBG-Hypoglycemia-Forecast . |
| format | Article |
| id | doaj-art-1f9d4293856548c0b03ed363aee60f2d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1f9d4293856548c0b03ed363aee60f2d2024-12-29T12:25:13ZengNature PortfolioScientific Reports2045-23222024-12-0114111610.1038/s41598-024-82649-4A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graphMert Onur Cakiroglu0Hasan Kurban1Lilia Aljihmani2Khalid Qaraqe3Goran Petrovski4Mehmet M. Dalkilic5Computer Science Department, Indiana UniversityCollege of Science and Engineering, Hamad Bin Khalifa UniversityElectrical and Computer Engineering, Texas A &M University at QatarCollege of Science and Engineering, Hamad Bin Khalifa UniversityWeill Cornell Medicine, Cornell UniversityComputer Science Department, Indiana UniversityAbstract Pediatric diabetes I is an endemic and an especially difficult disease; indeed, at this point, there does not exist a cure, but only careful management that relies on anticipating hypoglycemia. The changing physiology of children producing unique blood glucose signatures, coupled with inconsistent activities, e.g., playing, eating, napping, makes “forecasting” elusive. While work has been done for adult diabetes I, this does not successfully translate for children. In the work presented here, we adopt a reinforcement approach by leveraging the de Bruijn graph that has had success in detecting patterns in sequences of symbols–most notably, genomics and proteomics. We translate a continuous signal of blood glucose levels into an alphabet that then can be used to build a de Bruijn, with some extensions, to determine blood glucose states. The graph allows us to “tune” its efficacy by computationally ignoring edges that provide either no information or are not related to entering a hypoglycemic episode. We can then use paths in the graph to anticipate hypoglycemia in advance of about 30 minutes sufficient for a clinical setting and additionally find actionable rules that accurate and effective. All the code developed for this study can be found at: https://github.com/KurbanIntelligenceLab/dBG-Hypoglycemia-Forecast .https://doi.org/10.1038/s41598-024-82649-4 |
| spellingShingle | Mert Onur Cakiroglu Hasan Kurban Lilia Aljihmani Khalid Qaraqe Goran Petrovski Mehmet M. Dalkilic A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph Scientific Reports |
| title | A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph |
| title_full | A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph |
| title_fullStr | A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph |
| title_full_unstemmed | A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph |
| title_short | A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph |
| title_sort | reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes i patients using an extended de bruijn graph |
| url | https://doi.org/10.1038/s41598-024-82649-4 |
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