Big Data Analytics in Healthcare

Abstract

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

“…In general, medical image data range anywhere from a few megabytes for a single study to hundreds of megabytes per study (e.g. thinslice CT studies comprise of up to 2500+ scans per study) [3,4]. Such data require large storage capacities if stored for long term.…”

Big Data and Data-Driven Healthcare Systems

“…[10] Developing a detailed model of a human being by combining physiological data and high-throughput -omics techniques has the potential to enhance the knowledge of disease states and help develop blood-based diagnostic tools. Medical image analysis, signal processing of physiological data, and integration of physiological and -omics data face challenges and opportunities in dealing with disparate structured and unstructured big data sources [11]. Big data technologies are increasingly used for processing next-generation sequencing (NGS) data, motivated by the volume and velocity at which sequencing data is produced.…”

Big Data Knowledge System in Healthcare

“…Summary of medication records are depicted in Table 6. Unstructured Clinical Note: Clinical documentation is often in the form of unstructured and it is widely used to improve the disease diagnosis [15]. Clinical notes are also considered as big data and scalable algorithms are used to process such huge size of data.…”

“…For example, visualizing blood vessel structure can be done using CT, MRI, photoacoustic imaging, and ultrasound. The main challenge with the image data is that it is not only large size, but also complex and multi dimensional [15].…”

“…In order to overcome this issue, researchers are developed P4 medicine paradigm (i.e. predictive, preventive, participatory, and personalized health) with omics outline [15].…”