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      Medical big data: promise and challenges

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          Abstract

          The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.

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          Most cited references30

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          Machine Learning in Medicine.

          Rahul Deo (2015)
          Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
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            Randomized, Controlled Trials, Observational Studies, and the Hierarchy of Research Designs

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              The inevitable application of big data to health care.

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                Author and article information

                Journal
                Kidney Res Clin Pract
                Kidney Res Clin Pract
                Kidney Research and Clinical Practice
                Korean Society of Nephrology
                2211-9132
                2211-9140
                March 2017
                31 March 2017
                : 36
                : 1
                : 3-11
                Affiliations
                Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
                Author notes
                Correspondence: Hyung-Jin Yoon, Department of Biomedical Engineering, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea. E-mail: hjyoon@ 123456snu.ac.kr
                Article
                krcp-36-003
                10.23876/j.krcp.2017.36.1.3
                5331970
                28392994
                e91f6486-fa5a-49e1-b246-31df98272126
                Copyright © 2017 by The Korean Society of Nephrology

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 08 August 2016
                : 26 September 2016
                Categories
                Special Article

                big data,epidemiology,data mining,healthcare,statistics
                big data, epidemiology, data mining, healthcare, statistics

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