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| Full article title | Big data management for healthcare systems: Architecture, requirements, and implementation |
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| Journal | Advances in Bioinformatics |
| Author(s) | El aboudi, Naoual; Benhilma, Laila |
| Author affiliation(s) | Mohammed V University |
| Primary contact | Email: nawal dot elaboudi at gmail dot com |
| Editors | Fdez-Riverola, Florentino |
| Year published | 2018 |
| Volume and issue | 2018 |
| Page(s) | 4059018 |
| DOI | 10.1155/2018/4059018 |
| ISSN | 1687-8035 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.hindawi.com/journals/abi/2018/4059018/ |
| Download | http://downloads.hindawi.com/journals/abi/2018/4059018.pdf (PDF) |
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Abstract
The growing amount of data in the healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition. Based on the proposed architecture, a prototype implementation has been built for healthcare systems in order to generate real-time alerts. The suggested prototype is based on Spark and MongoDB tools.
Introduction
The proportion of elderly people in society is growing worldwide[1]; this phenomenon—referred to by the World Health Organization as "humanity’s aging"[1]—has many implications on healthcare services, especially in terms of cost. In the face of such a situation, relying on classical systems may result in a life quality decline for millions of people. Seeking to overcome this problem, a variety of different healthcare systems have been designed. Their common principle is transferring, on a periodical basis, medical parameters like blood pressure, heart rate, glucose level, body temperature, and ECG signals to an automated system aimed at monitoring in real time patients' health condition. Such systems provide quick assistance when needed since data is analyzed continuously. Automating health monitoring favors a proactive approach that relieves medical facilities by saving costs related to hospitalization, and it also enhances healthcare services by improving waiting time for consultations. Recently, the number of data sources in the healthcare industry has grown rapidly as a result of widespread use of mobile and wearable sensor technologies, which have flooded the healthcare arena with a huge amount of data. Therefore, it becomes challenging to perform healthcare data analysis based on traditional methods which are unfit to handle the high volume of diversified medical data. In general, the healthcare domain has four categories of analytics: descriptive, diagnostic, predictive, and prescriptive analytics. A brief description of each one of them is given below.
Descriptive analytics refers to describing current situations and reporting on them. Several techniques are employed to perform this level of analytics. For instance, descriptive statistics tools like histograms and charts are among the techniques used in descriptive analytics.
Diagnostic analysis aims to explain why certain events occurred and what the factors that triggered them are. For example, diagnostic analysis attempts to understand the reasons behind the regular readmission of some patients by using several methods such as clustering and decision trees.
Predictive analytics reflects the ability to predict future events; it also helps in identifying trends and determining probabilities of uncertain outcomes. An illustration of its role is to predict whether or not a patient will have complications. Predictive models are often built using machine learning techniques.
Prescriptive analytics proposes suitable actions leading to optimal decision-making. For instance, prescriptive analysis may suggest rejecting a given treatment in the case of a harmful side effect's high probability. Decision trees and Monte Carlo simulation are examples of methods applied to perform prescriptive analytics. Figure 1 illustrates analytics phases for the healthcare domain.[2] The integration of big data technologies into healthcare analytics may lead to better performance of medical systems.
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In fact, big data refers to large datasets that combine the following characteristics[3] : volume, which refers to high amounts of data; velocity, which means that data is generated at a rapid pace; variety, which emphasizes that data comes under different formats; and veracity, which means that data originates from a trustworthy sources.
Another characteristic of big data is variability. It indicates variations that occur in the data flow rates. Indeed, velocity does not provide a consistent description of the data due to its periodic peaks and troughs. Another important aspect of big data is complexity; it arises from the fact that big data is often produced through many sources, which implies to perform many operations over the data, these operations include identifying relationships and cleansing and transforming data flowing from different origins.
Moreover, Oracle decided to introduce value as a key attribute of big data. According to Oracle, big data has a “low value density,” which means that raw data has a low value compared to its high volume. Nevertheless, analysis of important volumes of data may lead to obtaining a high value.
In the context of healthcare, high volumes of data are generated by multiple medical sources, and it includes, for example, biomedical images, lab test reports, physician written notes, and health condition parameters allowing real-time patient health monitoring. In addition to its huge volume and its diversity, healthcare data flows at high speed. As a result, big data approaches offer tremendous opportunities regarding healthcare systems efficiency.
The contribution of this research paper is to propose an extensible big data architecture for healthcare applications formed by several components capable of storing, processing, and analyzing the significant amount of data in real time and batch modes. This paper demonstrates the potential of using big data analytics in the healthcare domain to find useful information in highly valuable data.
The paper has been organized as follows: In the next section, a background of big data computing approaches and big data platforms is provided. Recent contributions on big data for healthcare systems are reviewed in the section after. Then, in the section "An extensible big data architecture for healthcare," the components of the proposed big data architecture for healthcare are described. The implementation process is reported in the penultimate section, followed by conclusions, along with recommendations for future research.
References
- ↑ 1.0 1.1 World Health Organization; National Institute of Aging, ed. (October 2011). "Global Health and Aging". WHO. http://www.who.int/ageing/publications/global_health/en/.
- ↑ Gandomi, A.; Haider, M. (2015). "Beyond the hype: Big data concepts, methods, and analytics". International Journal of Information Management 35 (2): 137–44. doi:10.1016/j.ijinfomgt.2014.10.007.
- ↑ Chen, M.; Mao, S.; Liu, Y. (2014). "Big Data: A Survey". Mobile Networks and Applications 19 (2): 171–209. doi:10.1007/s11036-013-0489-0.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was cleaned up for smoother reading. In some cases important information was missing from the references, and that information was added.
