Literature DB >> 25102315

What's so different about big data?. A primer for clinicians trained to think epidemiologically.

Theodore J Iwashyna1, Vincent Liu.   

Abstract

The Big Data movement in computer science has brought dramatic changes in what counts as data, how those data are analyzed, and what can be done with those data. Although increasingly pervasive in the business world, it has only recently begun to influence clinical research and practice. As Big Data draws from different intellectual traditions than clinical epidemiology, the ideas may be less familiar to practicing clinicians. There is an increasing role of Big Data in health care, and it has tremendous potential. This Demystifying Data Seminar identifies four main strands in Big Data relevant to health care. The first is the inclusion of many new kinds of data elements into clinical research and operations, in a volume not previously routinely used. Second, Big Data asks different kinds of questions of data and emphasizes the usefulness of analyses that are explicitly associational but not causal. Third, Big Data brings new analytic approaches to bear on these questions. And fourth, Big Data embodies a new set of aspirations for a breaking down of distinctions between research data and operational data and their merging into a continuously learning health system.

Keywords:  automatic data processing; data collection; data mining; information systems; multilevel analyses

Mesh:

Year:  2014        PMID: 25102315      PMCID: PMC4214055          DOI: 10.1513/AnnalsATS.201405-185AS

Source DB:  PubMed          Journal:  Ann Am Thorac Soc        ISSN: 2325-6621


  4 in total

1.  Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database.

Authors:  Mohammed Saeed; Mauricio Villarroel; Andrew T Reisner; Gari Clifford; Li-Wei Lehman; George Moody; Thomas Heldt; Tin H Kyaw; Benjamin Moody; Roger G Mark
Journal:  Crit Care Med       Date:  2011-05       Impact factor: 7.598

2.  "Big data" in the intensive care unit. Closing the data loop.

Authors:  Leo Anthony Celi; Roger G Mark; David J Stone; Robert A Montgomery
Journal:  Am J Respir Crit Care Med       Date:  2013-06-01       Impact factor: 21.405

3.  Big data. The parable of Google Flu: traps in big data analysis.

Authors:  David Lazer; Ryan Kennedy; Gary King; Alessandro Vespignani
Journal:  Science       Date:  2014-03-14       Impact factor: 47.728

Review 4.  Big data and clinicians: a review on the state of the science.

Authors:  Weiqi Wang; Eswar Krishnan
Journal:  JMIR Med Inform       Date:  2014-01-17
  4 in total
  11 in total

1.  Prediction of Future Chronic Opioid Use Among Hospitalized Patients.

Authors:  S L Calcaterra; S Scarbro; M L Hull; A D Forber; I A Binswanger; K L Colborn
Journal:  J Gen Intern Med       Date:  2018-02-05       Impact factor: 5.128

2.  Understanding intensive care unit benchmarking.

Authors:  Jorge I F Salluh; Marcio Soares; Mark T Keegan
Journal:  Intensive Care Med       Date:  2017-03-15       Impact factor: 17.440

Review 3.  The intensive care medicine research agenda on septic shock.

Authors:  Anders Perner; Anthony C Gordon; Derek C Angus; Francois Lamontagne; Flavia Machado; James A Russell; Jean-Francois Timsit; John C Marshall; John Myburgh; Manu Shankar-Hari; Mervyn Singer
Journal:  Intensive Care Med       Date:  2017-05-12       Impact factor: 17.440

Review 4.  Big Data and Data Science in Critical Care.

Authors:  L Nelson Sanchez-Pinto; Yuan Luo; Matthew M Churpek
Journal:  Chest       Date:  2018-05-09       Impact factor: 9.410

5.  Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

Authors:  Matthew M Churpek; Trevor C Yuen; Christopher Winslow; David O Meltzer; Michael W Kattan; Dana P Edelson
Journal:  Crit Care Med       Date:  2016-02       Impact factor: 7.598

Review 6.  What Can We Learn About Drug Safety and Other Effects in the Era of Electronic Health Records and Big Data That We Would Not Be Able to Learn From Classic Epidemiology?

Authors:  Ali Zarrinpar; Ting-Yuan David Cheng; Zhiguang Huo
Journal:  J Surg Res       Date:  2019-10-22       Impact factor: 2.192

7.  Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data.

Authors:  Edward J Schenck; Katherine L Hoffman; Marika Cusick; Joseph Kabariti; Evan T Sholle; Thomas R Campion
Journal:  J Biomed Inform       Date:  2021-04-14       Impact factor: 8.000

8.  Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data.

Authors:  Juan C Rojas; Kyle A Carey; Dana P Edelson; Laura R Venable; Michael D Howell; Matthew M Churpek
Journal:  Ann Am Thorac Soc       Date:  2018-07

9.  When do confounding by indication and inadequate risk adjustment bias critical care studies? A simulation study.

Authors:  Michael W Sjoding; Kaiyi Luo; Melissa A Miller; Theodore J Iwashyna
Journal:  Crit Care       Date:  2015-04-30       Impact factor: 9.097

10.  Acute kidney injury in the era of big data: the 15(th) Consensus Conference of the Acute Dialysis Quality Initiative (ADQI).

Authors:  Sean M Bagshaw; Stuart L Goldstein; Claudio Ronco; John A Kellum
Journal:  Can J Kidney Health Dis       Date:  2016-02-26
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