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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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.
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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.
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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)....
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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
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]....
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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]....
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TL;DR: With the right approach, data mining can discover unexpected side effects and drug interactions that need to be addressed in the context of clinical practice.
Abstract: With the right approach, data mining can discover unexpected side effects and drug interactions.
17 citations
Journal Article•
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Abstract: Big data comes in many forms It comes as customer information and transactions contained in customer-relationship management and enterprise resourceplanning systems and HTML-based web stores It comes as information generated by machine-to-machine applications collecting data from smart meters, manufacturing sensors, equipment logs, trading systems data and call detail records compiled by fixed and mobile telecommunications companies Big data can come with big differences Some say that the `three Vs' of big data should more properly be tagged as the `three HVs': high-volume, high-variety, high-velocity, and high-veracity Apply those tags to the mountains of information posted on social network and blogging sites, including Facebook, Twitter and VouTube; the deluge of text contained in email and instant messages; not to mention audio and video files It is evident then that it's not necessarily the 'big-ness' of information that presents big-data applications and services with their greatest challenge, but the variety and the speed at which all that constantly changing information must be ingested, processed, aggregated, filtered, organised and fed back in a meaningful way for businesses to get some value out of it
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