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Big data analytics in healthcare: promise and potential

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TLDR
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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Exploring the path to big data analytics success in healthcare

TL;DR: This study proposes a big data analytics-enabled business value model in which the resource-based theory (RBT) and capability building view are used to explain how big data Analytics capabilities can be developed and what potential benefits can be obtained by these capabilities in the health care industries.
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Concurrence of big data analytics and healthcare: A systematic review

TL;DR: There is a paucity of information on evidence of real-world use of Big Data analytics in healthcare, and majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data Analytics in developing countries.
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TL;DR: Recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data are reviewed.
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TL;DR: This paper draws on complexity theory and investigates the configurations of resources and contextual factors that lead to performance gains from big data analytics investments to show that depending on the context, big data Analytics resources differ in significance when considering performance gains.
References
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Book

Big data: The next frontier for innovation, competition, and productivity

James Manyika
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Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data

TL;DR: This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform.
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Big Data analytics

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Proceedings ArticleDOI

Towards large-scale twitter mining for drug-related adverse events

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.
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Big Data Analytics: Turning Big Data into Big Money

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.
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