Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos
dc.contributor.advisor | León, Fabian | spa |
dc.contributor.author | Ospitia Forero, Miguel Angel | spa |
dc.creator.cedula | 11481915869 | spa |
dc.date.accessioned | 2024-01-24T19:26:20Z | |
dc.date.available | 2024-01-24T19:26:20Z | |
dc.date.issued | 2023-11-23 | spa |
dc.description.abstract | The international endocrine association has a consensus of metrics, which are used to assess glycemic variability from continuous glucose monitoring sensor measurements. Glucose monitoring records sample every 5 minutes and are useful for detecting episodes of hypo/hyperglycemia in patients with diabetes. Communication failures, device misuse and other reasons lead to data loss affecting the calculation of metrics. | eng |
dc.description.abstract | La asociación internacional de endocrinología cuenta con un consenso de métricas, las cuales son usadas para evaluar la variabilidad glucémica a partir de las mediciones de los sensores de monitoreo continuo de glucosa. Los registros de monitorización toman muestras cada 5 minutos y son útiles para la detección de episodios de hipo/hiperglucemia en pacientes con diabetes. Fallas en la comunicación, mal uso del dispositivo y otras razones llevan a pérdidas de datos (‘data gaps’) afectando el cálculo de las métricas. | spa |
dc.description.degreelevel | Pregrado | spa |
dc.description.degreename | Ingeniero(a) Mecatrónico(a) | spa |
dc.description.degreetype | Investigación | spa |
dc.description.notes | Presencial | spa |
dc.identifier.bibliographicCitation | Danne, T. (2017, November 10). International Consensus on Use of Continuous Glucose Monitoring. CONTINUOUS GLUCOSE MONITORING AND RISK OF HYPOGLYCEMIA. | spa |
dc.identifier.bibliographicCitation | Fabian Mauricio León Vargas, M. G.-J. (2018). Different Indexes of Glycemic Variability as Identifiers of Patients with Risk of Hypoglycemia in Type 2 Diabetes Mellitus. Journal of Diabetes Science and Technology, 1007-1015. | spa |
dc.identifier.bibliographicCitation | Maira A. García-Jaramillo, F. M. (2019). Impact of sensor-augmented pump therapy with predictive low-glucose management on hypoglycemia and glycemic control in patients with type 1 diabetes mellitus: 1-year follow-up. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2635-2631. | spa |
dc.identifier.bibliographicCitation | Martina. Drecogna, e. a. (2021). Data Gap Modeling in Continuous Glucose Monitoring Sensor Data. 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Virtual Conference . | spa |
dc.identifier.bibliographicCitation | MathWorks. (2023). MathWorks. Retrieved from One-sample Kolmogorov-Smirnov test: https://www.mathworks.com/help/stats/kstest.html | spa |
dc.identifier.bibliographicCitation | MathWorks. (2023). MathWorks. Retrieved from Two-sample F-test for equal variances: https://www.mathworks.com/help/stats/vartest2.html | spa |
dc.identifier.bibliographicCitation | Monnier, L. (2016). Toward Defining the Threshold Between Low and High Glucose Variability in Diabetes. CLINICAL CARE/EDUCATION/NUTRITION/PSYCHOSOCIAL RESEARCH, 832–838. | spa |
dc.identifier.bibliographicCitation | Nathan. R. Hil, e. a. (2011). Normal Reference Range for Mean Tissue Glucose and Glycemic Variability Derived from Continuous Glucose Monitoring for Subjects Without Diabetes in Different Ethnic Groups. DIABETES TECHNOLOGY & THERAPEUTICS, nº 201 921-928. | spa |
dc.identifier.bibliographicCitation | Peter. A. Baghurst, e. a. (2010). The Minimum Frequency of Glucose Measurements from Which Glycemic Variation Can Be Consistently Assessed. Journal of Diabetes Science and Technology, vol. IV, nº 6, 1382 - 1385. | spa |
dc.identifier.bibliographicCitation | Rodbard, D. (2011). Glycemic Variability: Measurement and Utility in Clinical Medicine and Research—One Viewpoint. DIABETES TECHNOLOGY & THERAPEUTICS,1077-1080. | spa |
dc.identifier.bibliographicCitation | Stephanie. J. Fonda, e. a. (2013). Minding the Gaps in Continuous Glucose Monitoring: A Method to Repair Gaps to Achieve More Accurate Glucometrics. Journal of Diabetes Science and Technology, vol. XII, 88-92. | spa |
dc.identifier.instname | instname:Universidad Antonio Nariño | spa |
dc.identifier.reponame | reponame:Repositorio Institucional UAN | spa |
dc.identifier.repourl | repourl:https://repositorio.uan.edu.co/ | spa |
dc.identifier.uri | http://repositorio.uan.edu.co/handle/123456789/9043 | |
dc.language.iso | spa | spa |
dc.publisher | Universidad Antonio Nariño | spa |
dc.publisher.campus | Bogotá - Sur | spa |
dc.publisher.faculty | Facultad de Ingeniería Mecánica, Electrónica y Biomédica | spa |
dc.publisher.program | Ingeniería Mecatrónica | spa |
dc.rights | Acceso abierto | |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
dc.rights.license | Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) | spa |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
dc.subject | Variabilidad glucémica | es_ES |
dc.subject | data gaps | es_ES |
dc.subject | métricas | es_ES |
dc.subject | precisión | es_ES |
dc.subject | análisis | es_ES |
dc.subject.ddc | 621.52 O839 | es_ES |
dc.subject.keyword | Glycemic variability | es_ES |
dc.subject.keyword | data gaps | es_ES |
dc.subject.keyword | metrics | es_ES |
dc.subject.keyword | accuracy | es_ES |
dc.subject.keyword | analysis | es_ES |
dc.title | Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos | es_ES |
dc.type | Trabajo de grado (Pregrado y/o Especialización) | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_7a1f | spa |
dc.type.coarversion | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dcterms.audience | General | spa |
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