Bio-inspired strategies for modeling and detection in diabetes mellitus treatment:
Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bio-inspired techniques such as modelling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural...
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, MA
Morgan Kaufmann
2024
|
| Ausgabe: | First edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9780443223402/?ar |
| Zusammenfassung: | Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bio-inspired techniques such as modelling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by the extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modelling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks in force in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with the extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modelling of data obtained by continuous glucose monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modelling, prediction, and classification.Addresses the online identification of diabetes mellitus using a high-order recurrent neural network trained online by an extended Kalman filter. Covers parametric identification of compartmental models used to describe diabetes mellitus. Provides modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia. |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | 1 Online-Ressource (250 Seiten) color illustrations |
| ISBN: | 9780443223402 0443223408 |
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| 245 | 1 | 0 | |a Bio-inspired strategies for modeling and detection in diabetes mellitus treatment |c Alma Y. Alanis, Oscar D. Sánchez, Alonso Vaca Gonzalez, Marco Perez Cisneros |
| 250 | |a First edition. | ||
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| 520 | |a Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bio-inspired techniques such as modelling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by the extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modelling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks in force in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with the extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modelling of data obtained by continuous glucose monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modelling, prediction, and classification.Addresses the online identification of diabetes mellitus using a high-order recurrent neural network trained online by an extended Kalman filter. Covers parametric identification of compartmental models used to describe diabetes mellitus. Provides modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia. | ||
| 650 | 0 | |a Diabetes |x Treatment | |
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| 700 | 1 | |a Sánchez, Oscar D. |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Pérez-Cisneros, Marco |e VerfasserIn |4 aut | |
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| author | Alanis, Alma Y. Sánchez, Oscar D. Gonzalez, Alonso Vaca Pérez-Cisneros, Marco |
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| discipline | Medizin |
| edition | First edition. |
| format | Electronic eBook |
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| isbn | 9780443223402 0443223408 |
| language | English |
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| spelling | Alanis, Alma Y. VerfasserIn aut Bio-inspired strategies for modeling and detection in diabetes mellitus treatment Alma Y. Alanis, Oscar D. Sánchez, Alonso Vaca Gonzalez, Marco Perez Cisneros First edition. Cambridge, MA Morgan Kaufmann 2024 1 Online-Ressource (250 Seiten) color illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bio-inspired techniques such as modelling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by the extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modelling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks in force in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with the extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modelling of data obtained by continuous glucose monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modelling, prediction, and classification.Addresses the online identification of diabetes mellitus using a high-order recurrent neural network trained online by an extended Kalman filter. Covers parametric identification of compartmental models used to describe diabetes mellitus. Provides modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia. Diabetes Treatment Biomedical engineering Génie biomédical biomedical engineering Sánchez, Oscar D. VerfasserIn aut Gonzalez, Alonso Vaca VerfasserIn aut Pérez-Cisneros, Marco VerfasserIn aut |
| spellingShingle | Alanis, Alma Y. Sánchez, Oscar D. Gonzalez, Alonso Vaca Pérez-Cisneros, Marco Bio-inspired strategies for modeling and detection in diabetes mellitus treatment Diabetes Treatment Biomedical engineering Génie biomédical biomedical engineering |
| title | Bio-inspired strategies for modeling and detection in diabetes mellitus treatment |
| title_auth | Bio-inspired strategies for modeling and detection in diabetes mellitus treatment |
| title_exact_search | Bio-inspired strategies for modeling and detection in diabetes mellitus treatment |
| title_full | Bio-inspired strategies for modeling and detection in diabetes mellitus treatment Alma Y. Alanis, Oscar D. Sánchez, Alonso Vaca Gonzalez, Marco Perez Cisneros |
| title_fullStr | Bio-inspired strategies for modeling and detection in diabetes mellitus treatment Alma Y. Alanis, Oscar D. Sánchez, Alonso Vaca Gonzalez, Marco Perez Cisneros |
| title_full_unstemmed | Bio-inspired strategies for modeling and detection in diabetes mellitus treatment Alma Y. Alanis, Oscar D. Sánchez, Alonso Vaca Gonzalez, Marco Perez Cisneros |
| title_short | Bio-inspired strategies for modeling and detection in diabetes mellitus treatment |
| title_sort | bio inspired strategies for modeling and detection in diabetes mellitus treatment |
| topic | Diabetes Treatment Biomedical engineering Génie biomédical biomedical engineering |
| topic_facet | Diabetes Treatment Biomedical engineering Génie biomédical biomedical engineering |
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