Abstract
Vital signs monitors in intensive and intermediate care units generate large amounts of data, most of which are not recorded nor taken advantage of. We propose a computer system that allows the automatic and early detection of the deterioration of critical patients, through the real-time processing and analysis of digital health data, including physiological waveform data generated by the medical monitors. Our system tries to emulate the behavior of an expert intensivist physician, giving recommendations for clinical decision making to reduce the uncertainty on diagnosis, treatment options and prognosis. In our previous works, we presented an real-time Big Data infrastructure built using free software technologies. In this paper we improve its data management. We present and evaluate three different data representation models in Apache Kafka. One of this models outperforms the other two in storage space use and delivery time of both real-time and historical data. Our results show that Kafka can be used for historical data storage. This in turn allows us to eliminate the NoSQL database of our previous system. Unlike other works, ours attempts to reduce the number of components to lower system overhead and administration complexity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agbo, C.C., Mahmoud, Q.H., Eklund, J.M.: An architecture for cloud-assisted clinical support system for patient monitoring and disease detection in mobile environments. In: Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, pp. 245–250, PervasiveHealth 2018, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3240925.3240944
Akhtar, U., Khattak, A.M., Lee, S.: Challenges in managing real-time data in health information system (HIS). In: Chang, C.K., Chiari, L., Cao, Y., Jin, H., Mokhtari, M., Aloulou, H. (eds.) ICOST 2016. LNCS, vol. 9677, pp. 305–313. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39601-9_27
Alconada Magliano, J.P., García, E.F.: La situación de la terapia intensiva y su contexto. Rev. Argent. de Terapia Intensiva 32(1) (2015). https://revista.sati.org.ar/index.php/MI/article/view/418
Balaji, S., Patil, M., McGregor, C.: A cloud based big data based online health analytics for rural NICUs and PICUs in India: opportunities and challenges. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 385–390. IEEE (2017)
Balladini, J., Bruno, P., Zurita, R., Orlandi, C.: An automatic and early detection of the deterioration of patients in intensive and intermediate care units: technological challenges and solutions. J. Comput. Sci. Technol. 18(03), 218–227 (2018). https://journal.info.unlp.edu.ar/JCST/article/view/1139
Balladini, J., et al.: A tool for improving the delivery of integrated intensive health care performance. Int. J. Integr. Care 19(4), 222 (2019)
Balladini, J., Rozas, C., Frati, F.E., Vicente, N., Orlandi, C.: Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital. J. Comput. Sci. Technol. 15(2), 61–67 (2015)
Belle, A., Thiagarajan, R., Soroushmehr, S., Navidi, F., Beard, D.A., Najarian, K.: Big data analytics in healthcare. BioMed Res. Int. 2015 (2015)
Blunt, M.C., Burchett, K.R.: Out-of-hours consultant cover and case-mix-adjusted mortality in intensive care. The Lancet 356(9231), 735–736 (2000)
Buchman, T.G., et al.: Innovative interdisciplinary strategies to address the intensivist shortage. Crit. Care Med. 45(2), 298–304 (2017)
Chen, D., et al.: Real-time or near real-time persisting daily healthcare data into HDFS and elasticsearch index inside a big data platform. IEEE Trans. Industr. Inf. 13(2), 595–606 (2017)
Goodwin, A.J., et al.: A practical approach to storage and retrieval of high-frequency physiological signals. Physiol. Meas. 41(3), 035008 (2020)
Han, H., Ryoo, H.C., Patrick, H.: An infrastructure of stream data mining, fusion and management for monitored patients. In: 19th IEEE Symposium on Computer-Based Medical Systems (CBMS 2006), pp. 461–468. IEEE (2006)
Herasevich, V., Keegan, M.T., Pickering, B.W.: The future of ICU prediction scores in the era of big data. J. ICU Manage. Pract. 16(2), 112–113 (2016)
Kaur, J., Mann, D.K.S.: AI based HealthCare platform for real time, predictive and prescriptive analytics using reactive programming. In: Journal of Physics: Conference Series 933, p. 012010, January 2018. https://doi.org/10.1088%2F1742-6596%2F933%2F1%2F012010
Khazaei, H., McGregor, C., Eklund, M., El-Khatib, K., Thommandram, A.: Toward a big data healthcare analytics system: a mathematical modeling perspective. In: 2014 IEEE World Congress on Services, pp. 208–215 (2014)
López-Martínez, F., Núñez-Valdez, E.R., García-Díaz, V., Bursac, Z.: A case study for a big data and machine learning platform to improve medical decision support in population health management. Algorithms 13(4), 102 (2020)
Matam, B.R., Duncan, H.: Technical challenges related to implementation of a formula one real time data acquisition and analysis system in a paediatric intensive care unit. J. Clin. Monit. Comput. 32(3), 559–569 (2017). https://doi.org/10.1007/s10877-017-0047-6
Mathukia, C., Fan, W., Vadyak, K., Biege, C., Krishnamurthy, M.: Modified early warning system improves patient safety and clinical outcomes in an academic community hospital. J. Community Hosp. Intern. Med. Perspect. 5(2), 26716 (2015)
Nemati, S., Holder, A., Razmi, F., Stanley, M.D., Clifford, G.D., Buchman, T.G.: An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit. Care Med. 46(4), 547–553 (2018)
Reiz, A.N., de la Hoz, M.A., García, M.S.: Big data analysis and machine learning in intensive care units. Medicina Intensiva (English Edition) 43(7), 416–426 (2019)
Salomon, G.: The intensivist shortage: is there a way around it? Healthcare. https://www.healthcareglobal.com/public-health/intensivist-shortage-there-way-around-it
Sanchez-Pinto, L.N., Luo, Y., Churpek, M.M.: Big data and data science in critical care. Chest 154(5), 1239–1248 (2018)
Tegtmeyer, K.: The pediatric intensive care unit of the future: technological advances in pediatric critical care medicine. In: Wheeler, D., Wong, H., Shanley, T. (eds.) Science and Practice of Pediatric Critical Care Medicine, pp. 1–7. Springer, London, London (2009). https://doi.org/10.1007/978-1-84800-921-9_14
Topol, E.J.: High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25(1), 44–56 (2019). https://doi.org/10.1038/s41591-018-0300-7
Wang, G., et al.: Building a replicated logging system with Apache Kafka. Proc. VLDB Endow. 8(12), 1654–1655 (2015). https://doi.org/10.14778/2824032.2824063
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Cañibano, R., Rozas, C., Orlandi, C., Balladini, J. (2020). Data Management Optimization in a Real-Time Big Data Analysis System for Intensive Care. In: Rucci, E., Naiouf, M., Chichizola, F., De Giusti, L. (eds) Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2020. Communications in Computer and Information Science, vol 1291. Springer, Cham. https://doi.org/10.1007/978-3-030-61218-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-61218-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61217-7
Online ISBN: 978-3-030-61218-4
eBook Packages: Computer ScienceComputer Science (R0)