Big Data Analytics and Intelligence by Tanwar Poonam; Jain Vishal; Liu Chuan-Ming

Author:Tanwar, Poonam; Jain, Vishal; Liu, Chuan-Ming
Language: eng
Format: epub
Publisher: Emerald Publishing Limited
Published: 2020-09-08T00:00:00+00:00

The above sections have inspired in assimilating the diverse heterogeneous data from multimodal domains which play a promising hand for effective large-scale data analysis. The readings from present-day smart sensors (for instance, glucose meters, heart meters, accelerometers, among others) with the combination of healthcare-related data from the social and web platforms picture a complete 360° wellbeing perspective of a patient (or a populace), and thus holds treasure house of immense useful patterns. Expending progressive BDA tools to patient profiles, scientists are competent to ascertain cost, characteristics, and upshots of care can assist in distinguishing the most clinically and financially savvy medications, proactively advance the precision medicine concept, the right care and right living, recognize persons who would profit from anticipatory care or lifestyle changes, decoding of human DNA in minutes (next-generation sequencing), accurately predict human behavior, non-invasive precise cure to diseases, prevent diseases and foil terrorist attacks, optimize marketing efforts, find cures for cancer, market recommendation, preventing outbreak, and much more.

The platform support for BDA such as Hadoop, MapReduce, Hive, Spark, Presto, Yarn, Pig, NoSQL databases (Oussous et al., 2018), is used by companies nowadays, as given in Table 1. Weeks before the actual hit of the H1N1 virus, Google had painted the probability of its swift spread as a paper in the scientific journal Nature. This is the power of BHD analytics which has the potential to completely shift the healthcare to new sights. The non-invasive early and precise disease diagnosis with low cost, high quality is the eventual objective of current data-driven healthcare systems. Also, the patient consensus on healthcare quality, care will never be 100% because of the accident’s biases, misapprehensions and other factors which nurtures a challenge from this collection of data to afford information such as provider ratings and improved guidance. As the medical field gears up for using large-scale software applications to analyze challenging streaming patient data, IBM foresees a 20% diminution inpatient mortality. That’s not just a return on investment though it is, that’s the miracle of unearthing the gold piles which lie beneath these data highlands. The chief establishments like Microsoft, Dell, IBM, and Oracle are revolutionary data-mining stages now building a way forward for therapeutic specialists to sojourn on top of patient data and make available amended therapeutic care. Besides its abundant gains in the healthcare segment, an argument here is ended about some real-life transforms the big data in the healthcare subdivision can afford (Table 1).

Profit is gained by shrinking healthcare costs (Villanov University, 2019 2018), providing patient-centric services, research/innovation promotion (Ren, Werner, Pazzi, & Boukerche, 2010), surveillance/detecting spreading diseases earlier (Hamilton, 2010), improving the treatment methods, health trend analysis (Chai, 2016), hospital quality monitoring, studying drug efficacy (White, 2014), saving lives using data mining, accessing data anywhere, point-of-care decision-making, innovative smart devices, genome sequencing, social health, Mobile Health, etc. (Fig. 4).

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