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Journal ArticleDOI

Big data analytics in healthcare: promise and potential

07 Feb 2014-Vol. 2, Iss: 1, pp 3-3
TL;DR: Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs, and its potential is great; however there remain challenges to overcome.
Abstract: Objective: To describe the promise and potential of big data analytics in healthcare. Methods: The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results: The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions: Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.

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Citations
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Journal ArticleDOI
TL;DR: This paper presents a comprehensive discussion on state-of-the-art big data technologies based on batch and stream data processing based on structuralism and functionalism paradigms and strengths and weaknesses of these technologies are analyzed.

964 citations

Journal ArticleDOI
TL;DR: The historical development, architectural design and component functionalities of big data analytics, including analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability and traceability are examined.

941 citations


Cites background from "Big data analytics in healthcare: p..."

  • ...…from business intelligence and decision support systems enable healthcare organizations to analyze an immense volume, variety and velocity of data across a wide range of healthcare networks to support evidence-based decision making and action taking (Watson, 2014; Raghupathi & Raghupathi, 2014)....

    [...]

Journal ArticleDOI
TL;DR: The proposed MeDShare system is blockchain-based and provides data provenance, auditing, and control for shared medical data in cloud repositories among big data entities and employs smart contracts and an access control mechanism to effectively track the behavior of the data.
Abstract: The dissemination of patients’ medical records results in diverse risks to patients’ privacy as malicious activities on these records cause severe damage to the reputation, finances, and so on of all parties related directly or indirectly to the data. Current methods to effectively manage and protect medical records have been proved to be insufficient. In this paper, we propose MeDShare, a system that addresses the issue of medical data sharing among medical big data custodians in a trust-less environment. The system is blockchain-based and provides data provenance, auditing, and control for shared medical data in cloud repositories among big data entities. MeDShare monitors entities that access data for malicious use from a data custodian system. In MeDShare, data transitions and sharing from one entity to the other, along with all actions performed on the MeDShare system, are recorded in a tamper-proof manner. The design employs smart contracts and an access control mechanism to effectively track the behavior of the data and revoke access to offending entities on detection of violation of permissions on data. The performance of MeDShare is comparable to current cutting edge solutions to data sharing among cloud service providers. By implementing MeDShare, cloud service providers and other data guardians will be able to achieve data provenance and auditing while sharing medical data with entities such as research and medical institutions with minimal risk to data privacy.

819 citations

Journal ArticleDOI
TL;DR: It is proposed that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other, and needs a multi-layer architecture.

725 citations


Cites background from "Big data analytics in healthcare: p..."

  • ...In other worlds, IoT eHealth has to handle the complexity of the data in terms of their variety, volume and velocity [123], [124], [125]....

    [...]

Journal ArticleDOI
TL;DR: This paper compiles, summarizes, and organizes machine learning challenges with Big Data, highlighting the cause–effect relationship by organizing challenges according to Big Data Vs or dimensions that instigated the issue: volume, velocity, variety, or veracity.
Abstract: The Big Data revolution promises to transform how we live, work, and think by enabling process optimization, empowering insight discovery and improving decision making. The realization of this grand potential relies on the ability to extract value from such massive data through data analytics; machine learning is at its core because of its ability to learn from data and provide data driven insights, decisions, and predictions. However, traditional machine learning approaches were developed in a different era, and thus are based upon multiple assumptions, such as the data set fitting entirely into memory, what unfortunately no longer holds true in this new context. These broken assumptions, together with the Big Data characteristics, are creating obstacles for the traditional techniques. Consequently, this paper compiles, summarizes, and organizes machine learning challenges with Big Data. In contrast to other research that discusses challenges, this work highlights the cause–effect relationship by organizing challenges according to Big Data Vs or dimensions that instigated the issue: volume, velocity, variety, or veracity. Moreover, emerging machine learning approaches and techniques are discussed in terms of how they are capable of handling the various challenges with the ultimate objective of helping practitioners select appropriate solutions for their use cases. Finally, a matrix relating the challenges and approaches is presented. Through this process, this paper provides a perspective on the domain, identifies research gaps and opportunities, and provides a strong foundation and encouragement for further research in the field of machine learning with Big Data.

592 citations


Cites background from "Big data analytics in healthcare: p..."

  • ...These Big Data possess tremendous potential in terms of business value in a variety of fields such as health care, biology, transportation, online advertising, energy management, and financial services [3], [4]....

    [...]

References
More filters
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13 May 2011
TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.
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Proceedings ArticleDOI
31 Dec 2012
TL;DR: This analysis illustrates that the Big Data analytics is a fast-growing, influential practice and a key enabler for the social business and is critical for success in the age of social media.
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811 citations

Proceedings ArticleDOI
29 Oct 2012
TL;DR: An approach to find drug users and potential adverse events by analyzing the content of twitter messages utilizing Natural Language Processing (NLP) and to build Support Vector Machine (SVM) classifiers is described, suggesting that daily-life social networking data could help early detection of important patient safety issues.
Abstract: Drug-related adverse events pose substantial risks to patients who consume post-market or Drug-related adverse events pose substantial risks to patients who consume post-market or investigational drugs. Early detection of adverse events benefits not only the drug regulators, but also the manufacturers for pharmacovigilance. Existing methods rely on patients' "spontaneous" self-reports that attest problems. The increasing popularity of social media platforms like the Twitter presents us a new information source for finding potential adverse events. Given the high frequency of user updates, mining Twitter messages can lead us to real-time pharmacovigilance. In this paper, we describe an approach to find drug users and potential adverse events by analyzing the content of twitter messages utilizing Natural Language Processing (NLP) and to build Support Vector Machine (SVM) classifiers. Due to the size nature of the dataset (i.e., 2 billion Tweets), the experiments were conducted on a High Performance Computing (HPC) platform using MapReduce, which exhibits the trend of big data analytics. The results suggest that daily-life social networking data could help early detection of important patient safety issues.

230 citations

Book
01 Mar 2021
TL;DR: Ohlhorst et al. as mentioned in this paper focused on the business and financial value of big data analytics and discussed how to turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities.
Abstract: Unique insights to implement big data analytics and reap big returns to your bottom lineFocusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantageTakes an in-depth look at the financial value of big data analyticsOffers tools and best practices for working with big dataOnce the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.

200 citations