IoT Peritoneal Dialysis: an approach exploring remote patient monitoring
DOI:
https://doi.org/10.5753/jbcs.2024.3201Keywords:
Internet of Things, Vital Sign, Peritoneal Dialisys, Remote Patient ManagementAbstract
It is estimated that 5.4 million people will undergo Renal Replacement Therapy by 2030. Peritoneal dialysis seems to be the most widespread form of home treatment for these patients, but it faces problems related to its adherence. Remote monitoring has the potential to increase treatment adherence. This work aims to design an approach that integrates: (i) a platform for the acquisition of vital signs and other parameters of a patient on peritoneal dialysis; (ii) an environment where customizable rules build Situation Science and, when necessary, send notifications to the medical team; and (iii) a signal and image visualization interface that can be accessed remotely.
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Agrawal, R., Imieliński, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data, pages 207-216. DOI: 10.1145/170035.170072.
Agrawal, R., Srikant, R., et al. (1994). Fast algorithms for mining association rules. In Proc. of the 20th VLDB Conference, volume 487, page 499. Available online [link].
Ahmed, M. U. (2017). An intelligent healthcare service to monitor vital signs in daily life-a case study on health-iot. Int. J. Eng. Res. Appl.(IJERA), 7(3):43-55. Available online [link].
Bourhis, P., Reutter, J. L., and Vrgovc, D. (2020). Json: Data model and query languages. Information Systems, 89:101478. DOI: 10.1016/j.is.2019.101478.
Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8):982-1003. DOI: 10.1287/mnsc.35.8.982.
Dhinakaran, D. and Prathap, P. J. (2022). Protection of data privacy from vulnerability using two-fish technique with apriori algorithm in data mining. The Journal of Supercomputing, 78(16):17559-17593. DOI: 10.1007/s11227-022-04517-0.
Diamantidis, C. J. and Becker, S. (2014). Health information technology (it) to improve the care of patients with chronic kidney disease (ckd). BMC nephrology, 15(1):7. DOI: 10.1186/1471-2369-15-7.
Dossin, T. and Goffin, E. (2019). When the color of peritoneal dialysis effluent can be used as a diagnostic tool. In Seminars in dialysis, volume 32, pages 72-79. Wiley Online Library. DOI: 10.1111/sdi.12740.
Dridi, A., Sassi, S., and Faiz, S. (2017). A smart iot platform for personalized healthcare monitoring using semantic technologies. In Tools with Artificial Intelligence (ICTAI), 2017 IEEE 29th International Conference on, pages 1198-1203. IEEE. DOI: 10.1109/ICTAI.2017.00182.
Foreman, K. J., Marquez, et al. (2018). Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. The Lancet, 392(10159):2052-2090. DOI: 10.1016/S0140-6736(18)31694-5.
Gaspar, G., Fabo, P., Kuba, M., Dudak, J., and Nemlaha, E. (2020). Micropython as a development platform for iot applications. Intelligent Algorithms in Software Engineering: Proceedings of the 9th Computer Science On-line Conference 2020, Volume 1 9, 1(9):388-394. DOI: 10.1007/978-3-030-51965-0_34.
Global Burden of Disease (2020). Global burden of disease. Available online [link].
Jaiswal, K., Sobhanayak, et al. (2018). An iot-cloud based smart healthcare monitoring system using container based virtual environment in edge device. In 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research (ICETIETR), pages 1-7. IEEE. DOI: 10.1109/ICETIETR.2018.8529141.
John, O. and Jha, V. (2019). Remote patient management in peritoneal dialysis: An answer to an unmet clinical need. In Remote Patient Management in Peritoneal Dialysis, volume 197, pages 99-112. Karger Publishers. DOI: 10.1159/000496305.
Johnson, A. E., Pollard, T. J., Shen, L., Li-wei, H. L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G. (2016). Mimic-iii, a freely accessible critical care database. Scientific data, 3:160035. DOI: 10.1038/sdata.2016.35.
Kromann, F. M. and Kromann, F. M. (2018). Mysql triggers. Beginning PHP and MySQL: From Novice to Professional, pages 717-731. DOI: 10.1007/978-1-4302-6044-8_30.
Kruse, R. L., Ewigman, B. G., and Tremblay, G. C. (2001). The zipper: a method for using personal identifiers to link data while preserving confidentiality. Child abuse & neglect, 25(9):1241-1248. DOI: 10.1016/S0145-2134(01)00265-4.
Levin, A., Tonelli, et al. (2017). Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy. The Lancet, 390(10105):1888-1917. DOI: 10.1016/S0140-6736(17)30788-2.
Li, H., Yu, L., and He, W. (2019). The impact of gdpr on global technology development.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology. Available online [link].
Liu, N., Kim, J., Jung, Y., Arisy, A., Nicdao, M. A., Mikaheal, M., Baldacchino, T., Khadra, M., and Sud, K. (2017). Remote monitoring systems for chronic patients on home hemodialysis: field test of a copresence-enhanced design. JMIR human factors, 4(3):e21. DOI: 10.2196/humanfactors.7078.
Lopes, J. L., de Souza, R. S., Geyer, C., da Costa, C., Barbosa, J., Pernas, A. M., and Yamin, A. (2014). A middleware architecture for dynamic adaptation in ubiquitous computing. Journal of Universal Compuerter Science, 20(9):1327-1351. Available online [link].
Maia, P., Baffa, A., Cavalcante, E., Delicato, F. C., Batista, T., and Pires, P. F. (2015). Uma plataforma de middleware para integraçao de dispositivos e desenvolvimento de aplicaç oes em e-health. Anais do XXXIII SBRC, pages 361-374. Available online [link].
Mohapatra, D., Tripathy, J., Mohanty, K. K., and Nayak, D. S. K. (2021). Interpretation of optimized hyper parameters in associative rule learning using eclat and apriori. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pages 879-882. IEEE. DOI: 10.1109/ICCMC51019.2021.9418049.
Nunes, J. A. W., Wallston, et al. (2011). Associations among perceived and objective disease knowledge and satisfaction with physician communication in patients with chronic kidney disease. Kidney international, 80(12):1344-1351. DOI: 10.1038/ki.2011.240.
Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37(1):43. Available online [link].
Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. Communications Surveys Tutorials, IEEE, 16(1):414-454. DOI: 0.1109/SURV.2013.042313.00197.
Raj, S., Ramesh, D., and Sethi, K. K. (2021). A Spark-based Apriori algorithm with reduced shuffle overhead. Journal of Supercomputing, 77(1):133-151. DOI: 10.1007/s11227-020-03253-7.
Rapôso, C. F. L., de Lima, H. M., de Oliveira Junior, W. F., Silva, P. A. F., and de Souza Barros, E. E. (2019). Lgpd-lei geral de proteção de dados pessoais em tecnologia da informação: Revisão sistemática. RACE-Revista de Administração do Cesmac, 4:58-67. DOI: 10.3131/race.v4i0.1035.
Rosner, M. H. and Ronco, C. (2012). Remote monitoring for continuous peritoneal dialysis. In Peritoneal Dialysis-State-of-the-Art 2012, volume 178, pages 68-73. Karger Publishers. DOI: 10.1159/000337812.
Serpeninova, Y., Makarenko, S., and Litvinova, M. (2020). Computer-assisted audit techniques: Classification and implementation by auditor. Public Policy and Accounting, (1 (1)):44-49. DOI: 10.26642/ppa-2020-1(1)-44-49.
Sezer, O. B., Dogdu, E., and Ozbayoglu, A. M. (2018). Context-aware computing, learning, and big data in internet of things: a survey. IEEE Internet of Things Journal, 5(1):1-27. DOI: 10.1109/JIOT.2017.2773600.
Shawkat, M., Badawi, M., El-ghamrawy, S., Arnous, R., and El-desoky, A. (2022). An optimized fp-growth algorithm for discovery of association rules. The Journal of Supercomputing, pages 1-28. DOI: 10.1007/s11227-021-04066-y.
Souza, R., Lopes, J., Geyer, C., Cardozo, A., Yamin, A., and Barbosa, J. (2018). An architecture for iot management targeted to context awareness of ubiquitous applications. Journal of Universal Computer Science, 24(10):1452-1471. DOI: 10.3217/jucs-024-10-1452.
Streiner, D. L. (2003). Starting at the beginning: an introduction to coefficient alpha and internal consistency. Journal of personality assessment, 80(1):99-103. DOI: 10.1207/S15327752JPA8001_18.
Taber, K. S. (2018). The use of cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6):1273-1296. DOI: 10.1007/s11165-016-9602-2.
Turnbull, J. (2014). The Docker Book: Containerization is the new virtualization. James Turnbull. Book.
Varol Altay, E. and Alatas, B. (2020). Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining. Journal of Ambient Intelligence and Humanized Computing, 11:3449-3469. DOI: 10.1007/s12652-019-01540-7.
Wallace, E. L., Rosner, et al. (2017). Remote patient management for home dialysis patients. Kidney international reports, 2(6):1009-1017. DOI: 10.1016/j.ekir.2017.07.010.
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Copyright (c) 2024 Rogério Albandes, Alexandre Souza, Rodrigo Lambrecht, Leandro Pieper, Franklin Barcellos, Adenauer Yamin
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