Diabetes tracking panel: an on-line information system to registration and management

Online hospital information systems enable health care providers to ensure information. Although nowadays there are great technological advances; in Colombia, the impact on the health sector has been low. As a result, there is an increasing deficiency in cities with less access to new technologies....

Full description

Autores:
Salcedo, Dixon
Cortes, Albeiro
Ternera, Yesid
Henríquez, Carlos
Martes, Leidy
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9305
Acceso en línea:
https://hdl.handle.net/11323/9305
https://doi.org/10.11591/eei.v11i3.3477
https://repositorio.cuc.edu.co/
Palabra clave:
Diabetes test system
Health care
Information system
Web services
Rights
openAccess
License
Atribución-CompartirIgual 4.0 Internacional (CC BY-SA 4.0)
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network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Diabetes tracking panel: an on-line information system to registration and management
title Diabetes tracking panel: an on-line information system to registration and management
spellingShingle Diabetes tracking panel: an on-line information system to registration and management
Diabetes test system
Health care
Information system
Web services
title_short Diabetes tracking panel: an on-line information system to registration and management
title_full Diabetes tracking panel: an on-line information system to registration and management
title_fullStr Diabetes tracking panel: an on-line information system to registration and management
title_full_unstemmed Diabetes tracking panel: an on-line information system to registration and management
title_sort Diabetes tracking panel: an on-line information system to registration and management
dc.creator.fl_str_mv Salcedo, Dixon
Cortes, Albeiro
Ternera, Yesid
Henríquez, Carlos
Martes, Leidy
dc.contributor.author.spa.fl_str_mv Salcedo, Dixon
Cortes, Albeiro
Ternera, Yesid
Henríquez, Carlos
Martes, Leidy
dc.subject.proposal.eng.fl_str_mv Diabetes test system
Health care
Information system
Web services
topic Diabetes test system
Health care
Information system
Web services
description Online hospital information systems enable health care providers to ensure information. Although nowadays there are great technological advances; in Colombia, the impact on the health sector has been low. As a result, there is an increasing deficiency in cities with less access to new technologies. Therefore, it is necessary for the government and health care providers to join efforts to expand the use of information technologies in the health area to improve the overall quality of the service provided. Therefore, this project introduces diabetes tracking panel tests system to improve the management process. The development system is based on several Open-Source platforms, such as MySQL, among others. Finally, we found that implemented system can reduce the time management of diabetes tests by the staff medical and assistance care personal.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-06-24T13:42:46Z
dc.date.available.none.fl_str_mv 2022-06-24T13:42:46Z
dc.date.issued.none.fl_str_mv 2022
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Salcedo, D., Cortes, A., Ternera, Y., Henriquez, C., & Martes, L. (2022). Diabetes tracking panel: an on-line information system to registration and management. Bulletin of Electrical Engineering and Informatics, 11(3), 1614-1623. doi:https://doi.org/10.11591/eei.v11i3.3477
dc.identifier.issn.spa.fl_str_mv 2089-3191
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dc.identifier.url.spa.fl_str_mv https://doi.org/10.11591/eei.v11i3.3477
dc.identifier.doi.spa.fl_str_mv 10.11591/eei.v11i3.3477
dc.identifier.eissn.spa.fl_str_mv 2302-9285
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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identifier_str_mv Salcedo, D., Cortes, A., Ternera, Y., Henriquez, C., & Martes, L. (2022). Diabetes tracking panel: an on-line information system to registration and management. Bulletin of Electrical Engineering and Informatics, 11(3), 1614-1623. doi:https://doi.org/10.11591/eei.v11i3.3477
2089-3191
10.11591/eei.v11i3.3477
2302-9285
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9305
https://doi.org/10.11591/eei.v11i3.3477
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Bulletin of Electrical Engineering and Informatics
dc.relation.references.spa.fl_str_mv [1]I. Kojta, M. Chacińska, and A. Błachnio-Zabielska, “Obesity, Bioactive Lipids, and Adipose Tissue Inflammation in Insulin Resistance,” Nutrients 2020, vol. 12, no. 5, p. 1305, May 2020, doi: 10.3390/NU12051305.
[2]W. Ling, Y. Huang, Y. M. Huang, R. R. Fan, Y. Sui, and H. L. Zhao, “Global trend of diabetes mortality attributed to vascular complications, 2000–2016,” Cardiovascular Diabetology, vol. 19, no. 1, Dec. 2020, doi: 10.1186/S12933-020-01159-5.
[3] K. Nørgaard, “Telemedicine Consultations and Diabetes Technology During COVID-19,” Journal of Diabetes Science and Technology, vol. 14, no. 4, pp. 767–768, Jul. 2020, doi: 10.1177/1932296820929378.
[4] Y. Zhou, J. Chi, W. Lv, and Y. Wang, “Obesity and diabetes as high-risk factors for severe coronavirus disease 2019 (Covid-19),” Diabetes/Metabolism Research and Reviews, vol. 37, no. 2, Feb. 2021, doi: 10.1002/DMRR.3377.
[5] M. J. Redondo et al., “The clinical consequences of heterogeneity within and between different diabetes types,” Diabetologia, vol. 63, no. 10, pp. 2040–2048, Oct. 2020, doi: 10.1007/S00125-020-05211-7.
[6] E. Abuelgasim et al., “Clinical overview of diabetes mellitus as a risk factor for cardiovascular death,” Reviews in Cardiovascular Medicine, vol. 22, no. 2, pp. 301–314, 2021, doi: 10.31083/j.rcm2202038.
[7] E. Ahlqvist, R. Prasad, and L. Groop, “Subtypes of type 2 diabetes determined from clinical parameters,” Am Diabetes Assoc, vol. 69, no. 10, pp. 2086–2093, 2020, doi: 10.2337/dbi20-0001.
[8] J. Wong and G. Mehta, “Efficacy of depression management in an integrated psychiatric-diabetes education clinic for comorbid depression and diabetes mellitus types 1 and 2,” Canadian Journal of Diabetes, vol. 44, no. 6, pp. 455-460, August 2020, doi: 10.1016/j.jcjd.2020.03.013.
[9] C.-H. Tseng, “Metformin and Risk of Malignant Brain Tumors in Patients with Type 2 Diabetes Mellitus,” Biomolecules, vol. 11, pp. 1-14, 2021, doi: 10.3390/biom11081226.
[10] O. Rozanska, A. Uruska, and D. Zozulinska-Ziolkiewicz, “Brain-derived neurotrophic factor and diabetes,” International Journal of Molecular Sciences, vol. 21, no. 3, pp. 1-12, 2020, doi: 10.3390/ijms21030841.
[11] S. Peric and T. M. Stulnig, “Diabetes and COVID-19: Disease—Management—People,” Wiener Klinische Wochenschrift, vol. 132, no. 13–14, pp. 356–361, Jul. 2020, doi: 10.1007/S00508-020-01672-3.
[12] Z. Wu, Y. Tang, and Q. Cheng, “Diabetes increases the mortality of patients with COVID-19: a meta-analysis,” Acta Diabetologica, vol. 58, no. 2, pp. 139–144, Feb. 2021, doi: 10.1007/S00592-020-01546-0.
[13] A. Mohammadinejad, M. Heydari, R. Kazemi Oskuee and M. Rezayi, “A Critical Systematic Review of Developing Aptasensors for Diagnosis and Detection of Diabetes Biomarkers,” Critical Reviews in Analytical Chemistry, 2021, pp. 1-23.
[14] B. A. Lipsky et al., “Guidelines on the diagnosis and treatment of foot infection in persons with diabetes (IWGDF 2019 update),” Wiley Online Library, vol. 36, no. S1, Mar. 2020, doi: 10.1002/dmrr.3280.
[15] A. M. Vaskovsky, M. S. Chvanova and M. B. Rebezov, "Creation of digital twins of neural network technology of personalization of food products for diabetics," 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), 2020, pp. 251-253, doi: 10.1109/DCNAIR50402.2020.9216776.
[16] S. Joachim, P. P. Jayaraman, A. R. M. Forkan, A. Morshed and N. Wickramasinghe, “Design and Development of a Diabetes Self-Management Platform: A Case for Responsible Information System Development,” Hawaii International Conference on System Sciences (HICSS-54), 2021, doi: 10.24251/HICSS.2021.459.
[17] A. U. Haq et al., “Intelligent machine learning approach for effective recognition of diabetes in E-healthcare using clinical data,” mdpi.com, vol. 20, 2020, doi: 10.3390/s20092649.
[18] D. Ramamoorthy, A. Bai and N. Nagarajan, “A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms,” Obesity Medicine, vol. 17, no. 13, Oct. 2019, doi: 10.1016/j.obmed.2019.100152.
[19] Md. Maniruzzaman, Md. J. Rahman, B. Ahammed and Md. M. Abedin, “Classification and prediction of diabetes disease using machine learning paradigm,” Health Information Science and Systems, vol. 8, no. 7, Dec. 2020, doi: 10.1007/S13755-019-0095-Z.
[20] M. Shuja, S. Mittal and M. Zaman, “Effective prediction of type ii diabetes mellitus using data mining classifiers and SMOTE,” Springer, pp. 195–211, 2020, doi: 10.1007/978-981-15-0222-4_17.
[21] T. Nibareke and J. Laassiri, “Using Big Data-machine learning models for diabetes prediction and flight delays analytics,” Journal of Big Data, vol. 7, no. 1, Dec. 2020, doi: 10.1186/S40537-020-00355-0.
[22] E. F. Ruiz-Ledesma, R. Palma-Orozco and E. Acosta-Gonzaga, “Framework proposal for adaptive mobile intelligent agents,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 5, pp. 2759–2770, Oct. 2021, doi: 10.11591/eei.v10i5.2841.
[23] D. Salcedo, “Design and implementation of an uv radiation monitoring system to the Neiva-Huila municipality,” Journal of Engineering and Applied Sciences, vol. 14, no. 24, pp. 4176-4182, Dec. 2019.
[24] D. Suárez, J. Solano, R. B. Martinez, M. A.- CESTA, and undefined 2020, “Sistema Inteligente para para la gestión automática de un generador eléctrico basado en la arquitectura del IoT,” repositorio.cuc.edu.co, 2020, Accessed: Dec. 05, 2021. [Online]. Available: https://repositorio.cuc.edu.co/handle/11323/8721
[25] A. C. Cabezas, D. S.-A. J. of, and undefined 2020, “Renal function panel: an information system for results tests management at the Huila department,” repositorio.cuc.edu.co, vol. 15, no. 19, 2020, Accessed: Dec. 05, 2021. [Online]. Available: https://repositorio.cuc.edu.co/handle/11323/7808.
[26] A. C. Cabezas, D. Salcedo and I. A. Villa, “Information system to management comprehensive metabolic panel tests in hospitals of Huila-Colombia department,” repositorio.cuc.edu.co, vol. 15, no. 20, pp. 2348-2355, Oct. 2020.
[27] Albeiro Cortes, Dixon Salcedo, Yesid Ternera, Carlos Henriquez, and Leidy Martes, “Diabetes Tracking Test System,”, Dec. 01, 2020, Online. [Available]: https://github.com/albecor/Medical_DiabetesTracking
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spelling Salcedo, DixonCortes, AlbeiroTernera, YesidHenríquez, CarlosMartes, Leidy2022-06-24T13:42:46Z2022-06-24T13:42:46Z2022Salcedo, D., Cortes, A., Ternera, Y., Henriquez, C., & Martes, L. (2022). Diabetes tracking panel: an on-line information system to registration and management. Bulletin of Electrical Engineering and Informatics, 11(3), 1614-1623. doi:https://doi.org/10.11591/eei.v11i3.34772089-3191https://hdl.handle.net/11323/9305https://doi.org/10.11591/eei.v11i3.347710.11591/eei.v11i3.34772302-9285Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Online hospital information systems enable health care providers to ensure information. Although nowadays there are great technological advances; in Colombia, the impact on the health sector has been low. As a result, there is an increasing deficiency in cities with less access to new technologies. Therefore, it is necessary for the government and health care providers to join efforts to expand the use of information technologies in the health area to improve the overall quality of the service provided. Therefore, this project introduces diabetes tracking panel tests system to improve the management process. The development system is based on several Open-Source platforms, such as MySQL, among others. Finally, we found that implemented system can reduce the time management of diabetes tests by the staff medical and assistance care personal.10 páginasapplication/pdfengInstitute of Advanced Engineering and Science (IAES)IndonesiaAtribución-CompartirIgual 4.0 Internacional (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Diabetes tracking panel: an on-line information system to registration and managementArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://beei.org/index.php/EEI/article/view/3477Bulletin of Electrical Engineering and Informatics[1]I. Kojta, M. Chacińska, and A. Błachnio-Zabielska, “Obesity, Bioactive Lipids, and Adipose Tissue Inflammation in Insulin Resistance,” Nutrients 2020, vol. 12, no. 5, p. 1305, May 2020, doi: 10.3390/NU12051305.[2]W. Ling, Y. Huang, Y. M. Huang, R. R. Fan, Y. Sui, and H. L. Zhao, “Global trend of diabetes mortality attributed to vascular complications, 2000–2016,” Cardiovascular Diabetology, vol. 19, no. 1, Dec. 2020, doi: 10.1186/S12933-020-01159-5.[3] K. Nørgaard, “Telemedicine Consultations and Diabetes Technology During COVID-19,” Journal of Diabetes Science and Technology, vol. 14, no. 4, pp. 767–768, Jul. 2020, doi: 10.1177/1932296820929378.[4] Y. Zhou, J. Chi, W. Lv, and Y. Wang, “Obesity and diabetes as high-risk factors for severe coronavirus disease 2019 (Covid-19),” Diabetes/Metabolism Research and Reviews, vol. 37, no. 2, Feb. 2021, doi: 10.1002/DMRR.3377.[5] M. J. Redondo et al., “The clinical consequences of heterogeneity within and between different diabetes types,” Diabetologia, vol. 63, no. 10, pp. 2040–2048, Oct. 2020, doi: 10.1007/S00125-020-05211-7.[6] E. Abuelgasim et al., “Clinical overview of diabetes mellitus as a risk factor for cardiovascular death,” Reviews in Cardiovascular Medicine, vol. 22, no. 2, pp. 301–314, 2021, doi: 10.31083/j.rcm2202038.[7] E. Ahlqvist, R. Prasad, and L. Groop, “Subtypes of type 2 diabetes determined from clinical parameters,” Am Diabetes Assoc, vol. 69, no. 10, pp. 2086–2093, 2020, doi: 10.2337/dbi20-0001.[8] J. Wong and G. Mehta, “Efficacy of depression management in an integrated psychiatric-diabetes education clinic for comorbid depression and diabetes mellitus types 1 and 2,” Canadian Journal of Diabetes, vol. 44, no. 6, pp. 455-460, August 2020, doi: 10.1016/j.jcjd.2020.03.013.[9] C.-H. Tseng, “Metformin and Risk of Malignant Brain Tumors in Patients with Type 2 Diabetes Mellitus,” Biomolecules, vol. 11, pp. 1-14, 2021, doi: 10.3390/biom11081226.[10] O. Rozanska, A. Uruska, and D. Zozulinska-Ziolkiewicz, “Brain-derived neurotrophic factor and diabetes,” International Journal of Molecular Sciences, vol. 21, no. 3, pp. 1-12, 2020, doi: 10.3390/ijms21030841.[11] S. Peric and T. M. Stulnig, “Diabetes and COVID-19: Disease—Management—People,” Wiener Klinische Wochenschrift, vol. 132, no. 13–14, pp. 356–361, Jul. 2020, doi: 10.1007/S00508-020-01672-3.[12] Z. Wu, Y. Tang, and Q. Cheng, “Diabetes increases the mortality of patients with COVID-19: a meta-analysis,” Acta Diabetologica, vol. 58, no. 2, pp. 139–144, Feb. 2021, doi: 10.1007/S00592-020-01546-0.[13] A. Mohammadinejad, M. Heydari, R. Kazemi Oskuee and M. Rezayi, “A Critical Systematic Review of Developing Aptasensors for Diagnosis and Detection of Diabetes Biomarkers,” Critical Reviews in Analytical Chemistry, 2021, pp. 1-23.[14] B. A. Lipsky et al., “Guidelines on the diagnosis and treatment of foot infection in persons with diabetes (IWGDF 2019 update),” Wiley Online Library, vol. 36, no. S1, Mar. 2020, doi: 10.1002/dmrr.3280.[15] A. M. Vaskovsky, M. S. Chvanova and M. B. Rebezov, "Creation of digital twins of neural network technology of personalization of food products for diabetics," 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), 2020, pp. 251-253, doi: 10.1109/DCNAIR50402.2020.9216776.[16] S. Joachim, P. P. Jayaraman, A. R. M. Forkan, A. Morshed and N. Wickramasinghe, “Design and Development of a Diabetes Self-Management Platform: A Case for Responsible Information System Development,” Hawaii International Conference on System Sciences (HICSS-54), 2021, doi: 10.24251/HICSS.2021.459.[17] A. U. Haq et al., “Intelligent machine learning approach for effective recognition of diabetes in E-healthcare using clinical data,” mdpi.com, vol. 20, 2020, doi: 10.3390/s20092649.[18] D. Ramamoorthy, A. Bai and N. Nagarajan, “A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms,” Obesity Medicine, vol. 17, no. 13, Oct. 2019, doi: 10.1016/j.obmed.2019.100152.[19] Md. Maniruzzaman, Md. J. Rahman, B. Ahammed and Md. M. Abedin, “Classification and prediction of diabetes disease using machine learning paradigm,” Health Information Science and Systems, vol. 8, no. 7, Dec. 2020, doi: 10.1007/S13755-019-0095-Z.[20] M. Shuja, S. Mittal and M. Zaman, “Effective prediction of type ii diabetes mellitus using data mining classifiers and SMOTE,” Springer, pp. 195–211, 2020, doi: 10.1007/978-981-15-0222-4_17.[21] T. Nibareke and J. Laassiri, “Using Big Data-machine learning models for diabetes prediction and flight delays analytics,” Journal of Big Data, vol. 7, no. 1, Dec. 2020, doi: 10.1186/S40537-020-00355-0.[22] E. F. Ruiz-Ledesma, R. Palma-Orozco and E. Acosta-Gonzaga, “Framework proposal for adaptive mobile intelligent agents,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 5, pp. 2759–2770, Oct. 2021, doi: 10.11591/eei.v10i5.2841.[23] D. Salcedo, “Design and implementation of an uv radiation monitoring system to the Neiva-Huila municipality,” Journal of Engineering and Applied Sciences, vol. 14, no. 24, pp. 4176-4182, Dec. 2019.[24] D. Suárez, J. Solano, R. B. Martinez, M. A.- CESTA, and undefined 2020, “Sistema Inteligente para para la gestión automática de un generador eléctrico basado en la arquitectura del IoT,” repositorio.cuc.edu.co, 2020, Accessed: Dec. 05, 2021. [Online]. Available: https://repositorio.cuc.edu.co/handle/11323/8721[25] A. C. Cabezas, D. S.-A. J. of, and undefined 2020, “Renal function panel: an information system for results tests management at the Huila department,” repositorio.cuc.edu.co, vol. 15, no. 19, 2020, Accessed: Dec. 05, 2021. [Online]. Available: https://repositorio.cuc.edu.co/handle/11323/7808.[26] A. C. Cabezas, D. Salcedo and I. A. Villa, “Information system to management comprehensive metabolic panel tests in hospitals of Huila-Colombia department,” repositorio.cuc.edu.co, vol. 15, no. 20, pp. 2348-2355, Oct. 2020.[27] Albeiro Cortes, Dixon Salcedo, Yesid Ternera, Carlos Henriquez, and Leidy Martes, “Diabetes Tracking Test System,”, Dec. 01, 2020, Online. 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