Leveraging Digital Twin Technology in the Healthcare Industry – A Machine Learning Based Approach
DOI:
https://doi.org/10.61841/3jescx12Keywords:
Machine Learning, Jupyter Notebook, Python, Decision Tree, Simulation, Digital Twin, IoT, HIPPA, Predictive Analytics, Prescriptive Analytics, Finite Element AnalysisAbstract
This paper deals with the concept of digital twin technology and leveraging the same in the healthcare domain. Digital twin technology is adding value to the healthcare industry by personalizing the diagnosis and therapy selection procedure. Finite Element Analysis (Finite Element Analysis, 2001) is a simulation method used to create a digital replica or a digital instance of the human organs such as the heart, kidney, or brain. IoT devices or sensors such as implantable cardioverter defibrillators or heart pacemakers collect the medical data of patients, which is analyzed to create a virtual instance using simulation software. The virtual instance is continuously updated and is used in generating reports for further diagnosis.
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References
1. Boschert, S. and Rosen, R. (2016). Digital Twin—The Simulation Aspect. Mechatronic Futures, 59-74.
2. Brosinsky, C., Westermann, D., and Krebs, R. (2018). Recent and prospective developments in power system control centers: adapting the digital twin technology for application in power system control centers.
3. Cunbo, Z., Jianhua, L., Hui, X., Xiaoyu, D., Shaoli, and Gang, W. (2017). Connotation, architecture, and trends of product digital twin. Computer-Integrated Manufacturing Systems.
4. Gochhait, S., Shou, D. T., and Fazalbhoy, S. (2020). Cloud Computing Applications and Techniques for E-Commerce. IGI Global. http://doi:10.4018/978-1-7998-1294-4
5. Houten, H. (2018). The rise of the digital twin: how healthcare can benefit.
6. Madni, A., Madni, C., and Lucero, S. (2019). Leveraging Digital Twin Technology in Model-Based Systems Engineering. MDPI, 13.
7. Roylance, D. (2001). Finite Element Analysis. 16.
8. Rimal, Y., and Gochhait, S. (2019). ”Machine Learning Neural Analysis Noisy Data”, International Journal of Engineering and Advanced Technology , ISSN: 2249-8958, 8(6), 08/2019
9. Sanders, R., Martin, R., Frumin, H., and Goldberg, M. (1984). Data Storage and Retrieval by Implantable Pacemakers for Diagnostic Purposes. Pacing Clin Electrophysiol, 1228-33.
10. Schleich, B., Anwer, N., Mathieu, L., and Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP Annals, 141-144.
11. Schroeder, G., Steinmetz, C., Pereira, C., and Espindola D. (2016). Digital twin data modeling with automationML and a communication methodology for data exchange. IFAC-PapersOnLine, 12-17.
12. Sivalingam, K., Sepulveda, M., Spring, M., and Davis, P. (2018). A Review and Methodology Development for Remaining Useful Life Prediction of Offshore Fixed and Floating Wind Turbine Power Converter with Digital Twin Technology Perspective.
13. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., and Sui, F. (2017). Digital twin-driven product design, manufacturing, and service with big data. The International Journal of Advanced Manufacturing Technology, 94.
14. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, C., and Nee, A. (2018). Digital twin-driven product design framework. International Journal of Production Research, 3935-3953.
15. Uhlemann, T., Lehmann, C., and Steinhilper, R. (2017). The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0. Procedia CIRP, 335-340.
16. Zhuang, C., Liu, J., and Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex product assembly shop floor. The International Journal of Advanced Manufacturing Technology, 94.
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