Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus
BACKGROUND: Widely available diabetes devices (continuous glucose monitoring, insulin pump etc.) generate large amount of data and development of an advanced clinical decision support system (CDSS), able to automatically evaluate and optimize insulin therapy, is relevant.AIM: Development of a mathem...
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Endocrinology Research Centre
2024-07-01
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| Series: | Сахарный диабет |
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| Online Access: | https://www.dia-endojournals.ru/jour/article/view/13167 |
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| author | D. Yu. Sorokin E. S. Trufanova O. Yu. Rebrova O. B. Bezlepkina D. N. Laptev |
| author_facet | D. Yu. Sorokin E. S. Trufanova O. Yu. Rebrova O. B. Bezlepkina D. N. Laptev |
| author_sort | D. Yu. Sorokin |
| collection | DOAJ |
| description | BACKGROUND: Widely available diabetes devices (continuous glucose monitoring, insulin pump etc.) generate large amount of data and development of an advanced clinical decision support system (CDSS), able to automatically evaluate and optimize insulin therapy, is relevant.AIM: Development of a mathematical model and an CDSS based on it to optimize insulin therapy in children with type 1 diabetes (T1D) and assessment of the agreement between the recommendations of the CDSS and the physician on insulin pump (IP) parameters: basal profile (BP), carbohydrate ratio (CR), correction factor (СF).MATERIALS AND METHODS: Data from 504 children with T1DM were analyzed over the period of 7875 days. The data included glucose, insulin, food, sex, age, height, weight, diabetes duration and HbA1c. We constructed recurrent neural network (RNN) to predict glucose concentration for 30-120 minutes, an algorithm for optimizing IP settings using prediction results. Next, a software product was developed — a CDSS. To assess the agreement of the recommendations of the CDSS and physicians, retrospective data from 40 remote telemedicine consultations of 40 patients with T1D (median age 11.6 years [7; 15]) were used and 960 points of possible adjustments were analyzed. Three degrees of agreement have been introduced: complete agreement, partial agreement, and complete disagreement. The magnitude of the adjustments was also analyzed.RESULTS: The accuracy of glycemic predictions was better or comparable with other similar models. The assessment of agreement for BP, CR and CF, according to the Kappa index, showed slight and weak agreement. The frequency of complete agreement between recommendations for adjusting the ongoing IP therapy between the CDSS and physicians is 37.5–53.8%, and complete inconsistency is 4.5–17.4%. From a clinical point of view, consistency in the frequency of occurrence of the indicator is more important. There were no differences in median IP settings between the CDSS and physicians.CONCLUSION: The CDSS has an acceptable accuracy of glycemic predictions. The CDSS and physicians provide comparable recommendations regarding CSII parameters. |
| format | Article |
| id | doaj-art-ee1709bbf00a493d8ae3a5e35ee67614 |
| institution | DOAJ |
| issn | 2072-0351 2072-0378 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Endocrinology Research Centre |
| record_format | Article |
| series | Сахарный диабет |
| spelling | doaj-art-ee1709bbf00a493d8ae3a5e35ee676142025-08-20T03:09:04ZengEndocrinology Research CentreСахарный диабет2072-03512072-03782024-07-0127324225310.14341/DM1316711095Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitusD. Yu. Sorokin0E. S. Trufanova1O. Yu. Rebrova2O. B. Bezlepkina3D. N. Laptev4Endocrinology Research CentreEndocrinology Research CentreEndocrinology Research Centre; Pirogov National Research Medical UniversityEndocrinology Research CentreEndocrinology Research CentreBACKGROUND: Widely available diabetes devices (continuous glucose monitoring, insulin pump etc.) generate large amount of data and development of an advanced clinical decision support system (CDSS), able to automatically evaluate and optimize insulin therapy, is relevant.AIM: Development of a mathematical model and an CDSS based on it to optimize insulin therapy in children with type 1 diabetes (T1D) and assessment of the agreement between the recommendations of the CDSS and the physician on insulin pump (IP) parameters: basal profile (BP), carbohydrate ratio (CR), correction factor (СF).MATERIALS AND METHODS: Data from 504 children with T1DM were analyzed over the period of 7875 days. The data included glucose, insulin, food, sex, age, height, weight, diabetes duration and HbA1c. We constructed recurrent neural network (RNN) to predict glucose concentration for 30-120 minutes, an algorithm for optimizing IP settings using prediction results. Next, a software product was developed — a CDSS. To assess the agreement of the recommendations of the CDSS and physicians, retrospective data from 40 remote telemedicine consultations of 40 patients with T1D (median age 11.6 years [7; 15]) were used and 960 points of possible adjustments were analyzed. Three degrees of agreement have been introduced: complete agreement, partial agreement, and complete disagreement. The magnitude of the adjustments was also analyzed.RESULTS: The accuracy of glycemic predictions was better or comparable with other similar models. The assessment of agreement for BP, CR and CF, according to the Kappa index, showed slight and weak agreement. The frequency of complete agreement between recommendations for adjusting the ongoing IP therapy between the CDSS and physicians is 37.5–53.8%, and complete inconsistency is 4.5–17.4%. From a clinical point of view, consistency in the frequency of occurrence of the indicator is more important. There were no differences in median IP settings between the CDSS and physicians.CONCLUSION: The CDSS has an acceptable accuracy of glycemic predictions. The CDSS and physicians provide comparable recommendations regarding CSII parameters.https://www.dia-endojournals.ru/jour/article/view/13167diabetes mellituschildrenartificial intelligenceinsulin pump therapyclinical decision support system |
| spellingShingle | D. Yu. Sorokin E. S. Trufanova O. Yu. Rebrova O. B. Bezlepkina D. N. Laptev Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus Сахарный диабет diabetes mellitus children artificial intelligence insulin pump therapy clinical decision support system |
| title | Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus |
| title_full | Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus |
| title_fullStr | Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus |
| title_full_unstemmed | Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus |
| title_short | Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus |
| title_sort | clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus |
| topic | diabetes mellitus children artificial intelligence insulin pump therapy clinical decision support system |
| url | https://www.dia-endojournals.ru/jour/article/view/13167 |
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