Ubiquitous Health Technology Management (uHTM)
Tóm tắt
The COVID-19 pandemic increased the need for distributed and ubiquitous health technology management. The eminent risk of Sars-CoV-2 contamination when visiting a health care establishment requires an efficient allocation of the technical team. The equipment problems should be quickly identified and fixed to keep the facility working at its full condition. This article presents a solution to perform remote real-time analysis of primary health care technology behavior, detecting and diagnosing the failures to create predictive maintenance plans. The project uses feature engineering to adapt regular machine learning algorithms to multiclass classification of time series data. The methodology was applied to a dental air compressor. It includes data collection, analysis, and exhibition. The model verified the IBM Watson and the Microsoft Azure Machine Learning Studio with the algorithms of neural networks, logistic regression, decision jungle, and decision forest, which was the most suitable one. The transformation performed in the data considered the influence of time in the read values to obtain a more efficient result in the platform. The solution integrated data collected by the sensors with the cloud using an Internet of Things architecture, a web service, and python scripts to exhibit the outcomes on the computer screen. Therefore, the model performs notification and identification of health technology failures, supporting the decision-making process of ubiquitous management in clinical engineering.
Tài liệu tham khảo
Awad M, Khanna R (2015) Efficient learning machines: theories, concepts, and applications for engineers and system designers. Apress, Berkeley, CA https://books.google.com.br/books?id=Bk4nCgAAQBAJ
Çoban S, Gökalp MO, Gökalp E, Eren, PE, Koçyiğit A (2018) Predictive maintenance in healthcare services with big data technologies. In: 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA), pp. 93–98. https://doi.org/10.1109/SOCA.2018.00021
Garcia R, Pezzolla G, Soares Filho R, Martins J (2018) Health technology ubiquitous management model for primary health care. In: Fourth WHO Global Forum on Medical Devices Report. Visakhapat-nam, India
Garcia S, Santos R, de Avelar P, Zaniboni R, Garcia R (2011) Health care technology management applied to public primary care health. In: 2011 Pan American Health Care Exchanges, pp. 250–253. https://doi.org/10.1109/PAHCE.2011.5871898
Guresen E, Kayakutlu G (2011) Definition of artificial neural networks with comparison to other networks. Proc Comp Sci 3:426–433. https://doi.org/10.1016/j.procs.2010.12.071
Hand D, Christen P (2018) A note on using the f-measure for evaluating record linkage algorithms. Statistics and Computing 28(3), 539–547 https://doi.org/10.1007/s11222-017-9746-6, https://app.dimensions.ai/details/publication/pub.1084928040 and http://spiral.imperial.ac.uk/bitstream/10044/1/46235/2/stco-d-16-00349-final.pdf
He H, Bai Y, Garcia EA, Li S (2008) Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), pp. 1322–1328
Idoine C, Krensky P, Brethenoux E, Hare J, Sicular S, Vashisth S (2018) Magic quadrant for data science and machine-learning platforms. Gartner, Inc
Kleinbaum DG, Dietz K, Gail M, Klein M, Klein M (2002) Logistic Regression: A Self-Learning Text. Springer, New York
Ling CX, Sheng VS (2010) Encyclopedia of Machine Learning. Springer US, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_110
Maktoubian J, Ansari K (2019) An iot architecture for preventive maintenance of medical devices in healthcare organizations. Health Technol 9(3):233–243. https://doi.org/10.1007/s12553-018-00286-0
Mobley R (2002) An Introduction to Predictive Maintenance, second edition edn. Butterworth-Heinemann, Burlington (2002). https://doi.org/10.1016/B978-075067531-4/50002-6, http://www.sciencedirect.com/science/article/pii/B9780750675314500026
Opitz J, Burst S (2019) Macro f1 and macro f1. arXiv e-prints p., https://ui.adsabs.harvard.edu/abs/2019arXiv191103347O
Oza N, Tumer K (2008) Classifier ensembles: Select real-world applications. Inform Fusion 9(1):4–20. https://doi.org/10.1016/j.inffus.2007.07.002
Ren Q, Ma X, Miao G (2005) Application of support vector machines in reciprocating compressor valve fault diagnosis. In: Advances in Natural Computation, pp. 81–84. Berlin, Heidelberg https://doi.org/10.1007/11539117_13
Rokach L (2016) Decision forest: Twenty years of research. Inform Fusion 27:111–125. https://doi.org/10.1016/j.inffus.2015.06.005
Schütze H, Manning CD, Raghavan P (2008) Introduction to Information Retrieval. Cambridge University Press, Cambridge
Shamayleh A, Awad M, Farhat J (2020) Iot based predictive maintenance management of medical equipment. J Med Sys 44(4):72. https://doi.org/10.1007/s10916-020-1534-8
Shotton J, Sharp T, Kohli P, Nowozin S, Winn J, Criminisi A (2013) Decision jungles: Compact and rich models for classification. In: Adv Neural Inf Process Syst 26, pp. 234–242. Red Hook
Soares Filho R, Martins J, Garcia R (2020) Methodology for defining ubiquitous management indicators in primary health care. In: VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering, pp. 1298–1305. Cham https://doi.org/10.1007/978-3-030-30648-9_167
Tong W, Hong H, Fang H, Xie Q, Perkins R (2003) Decision forest: Combining the predictions of multiple independent decision tree models. J Chem Info Comp Sci 43(2):525–531. https://doi.org/10.1021/ci020058s
Zambuto RP (2004) Introduction to Clinical Engineering, pp. 1–2. Biomedical Engineering. Academic Press, Burlington https://www.sciencedirect.com/science/article/pii/B9780122265709500028