.
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6. Anand, J. et al., Efficient Data Storage in Desktop Data-Grid Computing using Real-Time Parameters. Int. J. Comput. Sci. Technol., 2, 3, 392–397, 2011.
7. Kuswanto, and Kuswanto, Bayesian Model Averaging with Markov Chain Monte Carlo for Calibrating Temperature Forecast from Combination of Time Series Models. J. Math. Stat., 9, 349–356, 2013, https://doi.org/10.3844/jmssp.2013.349.356.
8. Minsley, B.J., A Trans-Dimensional Bayesian Markov Chain Monte Carlo Algorithm for Model Assessment Using Frequency-Domain Electromagnetic Data. Geophys. J. Int., 187, 252–272 2011, https://doi.org/10.1111/j.1365-246x.2011.05165.x.
9. Padli, J., Habibullah, M.S., Baharom, A.H., The Impact of Human Development on Natural Disaster Fatalities and Damage: Panel Data Evidence. Econ. Res.-Ekon. Istraž., 7, 1–17, 2018, https://doi.org/10.1080/1331677x.2018.1504689.
10. Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M.P., Shyu, M.-L., Chen, S.-C., Iyengar, S.S., A Survey on Deep Learning. ACM Comput. Surv., 22, 1–23, 2019, https://doi.org/10.1145/3234150.
11. R. Stuart, J., Stuart, J.R., Norvig, P., Davis, E., Artificial Intelligence: A Modern Approach, Prentice Hall, Springer, New Jersey, 2010.
12. Vista, A., Awwal, N., Care, E., Sequential Actions as Markers of Behavioural and Cognitive Processes: Extracting Empirical Pathways from Data Streams of Complex Tasks. Comput. Educ., 92–93, 15–36, 2016, https://doi.org/10.1016/j.compedu.2015.10.009.
13. Wollsen, M.G. and Jørgensen, B.N., Improved Local Weather Forecasts Using Artificial Neural Networks. Distributed Computing and Artificial Intelligence, 12th International Conference, 2015, https://doi.org/10.1007/978-3-319-19638-1_9.
14. On Sequential Spectral Analysis of Amplitude-Modulated Time Series, in: Sequential Analysis.
15. Habermann, A.N., Engineering Large Knowledge-Based Systems. Data Knowl. Eng., 15, 105–117, 1990, https://doi.org/10.1016/0169-023x(90)90007-z.
16. Kellett, J.M., Winstanley, G., Boardman, J.T., A Methodology for Knowledge Engineering Using an Interactive Graphical Tool for Knowledge Modelling. Artif. Intell. Eng., 4, 92–102, 1989, https://doi.org/10.1016/0954-1810(89)90004-6.
17. Knowledge Management and Knowledge Engineering. Knowl. Manage., 152, 2001, https://doi.org/10.1201/9781420041125.ch1.
18. Mohamudally, N., Introductory Chapter: Time Series Analysis (TSA) for Anomaly Detection in IoT, in: Time Series Analysis and Applications, 2018.
19. Motta, E., THE KNOWLEDGE MODELING PARADIGM IN KNOWLEDGE ENGINEERING, in: Handbook of Software Engineering and Knowledge Engineering, 2001.
20. Niharika, M., Deepa, N., Devi., T., Image segmentation and detection for healthcare data in deep learning, in: Test Engineering and Management, vol. 81, pp. 5402–5407, 2019.
21. Vaishnavi, R. et al., Efficient Security for Desktop Data Grid using Cryptographic Protocol. Proceedings of IEEE International Conference on Control, Automation, Communication and Energy Conservation, vol. 1, pp. 305–211, 2009.
22. Śmietanowski, M., Nonlinear Parameters Estimation from Sequential Short Time Data Series. Auton. Neurosci., 90, 158–166, 2001, https://doi.org/10.1016/s1566-0702(01)00283-1.
23. Zhang, Y., Zhong, M., Geng, N., Jiang., Y., Forecasting Electric Vehicles Sales with Univariate and Multivariate Time Series Models: The Case of China. PloS One, 12, 5, e0176729, 2017.
24. Gunn, S.W.A., The Scientific Basis of Disaster Management. Disaster Prev. Manag.: An Int. J., 1, 446 1992, https://doi.org/10.1108/09653569210018672.
1 *Corresponding author: [email protected]
3
Big Data Knowledge System in Healthcare
P. Sujatha1*, K. Mahalakshmi2 and P. Sripriya3
1Department of Information Technology, Vels Institute of Science, Technology & Advanced Studies, Pallavaram, Chennai, India
2School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies, Pallavaram, Chennai, India & Women’s Christian College, Chennai, India
3Department of Computer Applications, Vels Institute of Science, Technology & Advanced Studies, Pallavaram, Chennai, India
Abstract
The present society is an Informational Society. The information is essential for industries for accurate results. Knowledge systems integrate the information from multiple sources to extract important insights for decision-making. Due to digitalization, the data available is abundant in all kinds of industry. In order to handle voluminous data and to predict the outcomes accurately, a new framework is evolved using big data and big data analytics. Big data has huge implications for knowledge management. Medical data in the healthcare industry is enormous. In recent years, the healthcare sector is changing from volume-based service industry into value-based service industry, because health is the most precious gift to human beings. Advanced technologies and its related analytical tools increase better healthcare practices and drive human beings into longer life spans. Big data knowledge system has a significant responsibility in the healthcare practices. Big data knowledge systems analyze the large volume of patients’ electronic health records and transform the medical information into knowledge for assisting the process of clinical decisions, for recommendation of medicines, and for better diagnoses of diseases. In this chapter, we discuss big data, its applications, and challenges of the big data knowledge system in the healthcare sector
Keywords: Knowledge system, big data, electronic health records, healthcare, big data knowledge system, big data analytics, medical data, clinical decisions
3.1 Introduction
Since the last decade, there is a constant development in information technology. The most recent trends in technology like social networks, smart phones, computers, and smart wearable devices lead to the growth of data. This growth in the data has given embarks on this latest notion described as big data. Big data is a large data set, which is generated at rapid speed. This СКАЧАТЬ