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Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital
Keywords:
Big Data, Cloud Computing, Clinical Decision Support System, Real Time, Intensive Care UnitsAbstract
In a typical intensive care unit of a healthcare facilities, many sensors are connected to patients to measure high frequency physiological data. Currently, measurements are registered from time to time, possibly every hour. With this data lost, we are losing many opportunities to discover new patterns in vital signs that could lead to earlier detection of pathologies. The early detection of pathologies gives physicians the ability to plan and begin treatments sooner or potentially stop the progression of a condition, possibly reducing mortality and costs. The data generated by medical equipment are a Big Data problem with near real-time restrictions for processing medical algorithms designed to predict pathologies. This type of system is known as realtime big data analytics systems. This paper analyses if proposed system architectures can be applied in the Francisco Lopez Lima Hospital (FLLH), an Argentinian hospital with relatively high financial constraints. Taking into account this limitation, we describe a possible architectural approach for the FLLH, a mix of a local computing system at FLLH and a public cloud computing platform. We believe this work may be useful to promote the research and development of such systems in intensive care units of hospitals with similar characteristics to the FLLH.
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References
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[2] C. McGregor, “Big data in neonatal intensive care,” Computer, vol. 46, no. 6, pp. 54–59, 2013.
[3] P. Kligfield, L. S. Gettes, J. J. Bailey, R. Childers, B. J. Deal, E. W. Hancock, G. van Herpen, J. A. Kors, P. Macfarlane, D. M. Mirvis, O. Pahlm, P. Rautaharju, and G. S. Wagner, “Recommendations for the Standardization and Interpretation of the Electrocardiogram: Part I: The Electrocardiogram and Its Technology: A Scientific Statement From the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society Endorsed by the International Society for Computerized Electrocardiology,” Circulation, vol. 115, no. 10, pp. 1306–1324, 2007.
[4] M. Barlow, Real-Time Big Data Analytics: Emerging Architecture. O’Reilly, Jun. 2013.
[5] K. D. Fairchild, “Predictive monitoring for early detection of sepsis in neonatal icu patients.” Curr Opin Pediatr, 2013.
[6] H. Lee, C. G. Rusin, D. E. Lake, M. T. Clark, L. Guin, T. J. Smoot, A. O. Paget-Brown, B. D. Vergales, J. Kattwinkel, J. R. Moorman, and J. B. Delos, “A new algorithm for detecting central apnea in neonates,” Physiological Measurement, vol. 33, no. 1, p. 1, 2012.
[7] M. Blount, M. Ebling, J. Eklund, A. James, C. Mc-Gregor, N. Percival, K. Smith, and D. Sow, “Real-time analysis for intensive care: Development and deployment of the artemis analytic system,” Engineering in Medicine and Biology Magazine, IEEE, vol. 29, no. 2, pp. 110–118, March 2010.
[8] C. McGregor, “A cloud computing framework for real-time rural and remote service of critical care,” in Computer-Based Medical Systems (CBMS), 2011 24th International Symposium on, June 2011, pp. 1–6.
[9] B. Gedik, H. Andrade, K.-L. Wu, P. S. Yu, and M. Doo, “Spade: The system s declarative stream processing engine,” in Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD ’08. New York, NY, USA: ACM, 2008, pp. 1123–1134.
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Published
2015-11-01
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[1]
“Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital”, JCS&T, vol. 15, no. 02, pp. p. 61–67, Nov. 2015, Accessed: Jun. 15, 2025. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/553
