Advertisement

Intensive Care Medicine

, Volume 44, Issue 9, pp 1524–1527 | Cite as

What’s new in ICU in 2050: big data and machine learning

  • Sébastien Bailly
  • Geert Meyfroidt
  • Jean-François TimsitEmail author
What's New in Intensive Care

The era of big data

The amount of digitalized data that the world produces today is by all measures unseen and spectacular. Social media, e-commerce, and the Internet of things generate approximately 2.5 quintillions of bytes per day, an amount that equals 100 million Blu-ray discs, or almost 30,000 GB per second. Data grows exponentially, and 90% of all data on the Internet has been created since 2016. This trend will continue in the next decades [1]. Such datasets of unimaginable size cannot be maintained with traditional database management technology, or examined with traditional statistical techniques. The general term for methods to manage and analyze such unstructured datasets is “big data”. Although the term is often ill-defined and improperly used, the five “Vs” concept is a good summary: Volume, Velocity, Variety, Veracity, and Value referring to, respectively, the large quantity of data; the speed of acquisition; the diversity of data sources; the uncertain data quality;...

Notes

Compliance with ethical standards

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

References

  1. 1.
    Ffoulkes P (2017) InsideBIGDATA guide to the intelligent use of big data on an industrial scale. InsideBIGDATA, MassachusettsGoogle Scholar
  2. 2.
    Booth CM, Tannock IF (2014) Randomised controlled trials and population-based observational research: partners in the evolution of medical evidence. Br J Cancer 110:551–555CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Apkon M, Singhaviranon P (2001) Impact of an electronic information system on physician workflow and data collection in the intensive care unit. Intensive Care Med 27:122–130CrossRefPubMedGoogle Scholar
  4. 4.
    Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2:3CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Murdoch TB, Detsky AS (2013) The inevitable application of big data to health care. JAMA 309:1351–1352CrossRefPubMedGoogle Scholar
  6. 6.
    Flechet M, Grandas FG, Meyfroidt G (2016) Informatics in neurocritical care: new ideas for Big Data. Curr Opin Crit Care 22:87–93PubMedGoogle Scholar
  7. 7.
    Simpkin AL, Schwartzstein RM (2016) Tolerating uncertainty—the next medical revolution? N Engl J Med 375:1713–1715CrossRefPubMedGoogle Scholar
  8. 8.
    Angus DC (2015) Fusing randomized trials with big data: the key to self-learning health care systems? JAMA 314:767–768CrossRefPubMedGoogle Scholar
  9. 9.
    Mirnezami R, Nicholson J, Darzi A (2012) Preparing for precision medicine. N Engl J Med 366:489–491CrossRefPubMedGoogle Scholar
  10. 10.
    Perner A, Gordon AC, Angus DC, Lamontagne F, Machado F, Russell JA, Timsit JF, Marshall JC, Myburgh J, Shankar-Hari M, Singer M (2017) The intensive care medicine research agenda on septic shock. Intensive Care Med 43:1294–1305Google Scholar
  11. 11.
    Robins JM, Hernan MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology. Epidemiology 11:550–560CrossRefPubMedGoogle Scholar
  12. 12.
    Bailly S, Bouadma L, Azoulay E, Orgeas MG, Adrie C, Souweine B, Schwebel C, Maubon D, Hamidfar-Roy R, Darmon M, Wolff M, Cornet M, Timsit JF (2015) Failure of empirical systemic antifungal therapy in mechanically ventilated critically ill patients. Am J Respir Crit Care Med 191:1139–1146CrossRefPubMedGoogle Scholar
  13. 13.
    Timsit JF, Azoulay E, Schwebel C, Charles PE, Cornet M, Souweine B, Klouche K, Jaber S, Trouillet JL, Bruneel F, Argaud L, Cousson J, Meziani F, Gruson D, Paris A, Darmon M, Garrouste-Orgeas M, Navellou JC, Foucrier A, Allaouchiche B, Das V, Gangneux JP, Ruckly S, Maubon D, Jullien V, Wolff M, EMPIRICUS Trial Group (2016) Empirical micafungin treatment and survival without invasive fungal infection in adults with ICU-acquired sepsis, candida colonization, and multiple organ failure: the EMPIRICUS randomized clinical trial. JAMA 316:1555–1564CrossRefPubMedGoogle Scholar
  14. 14.
    Guiza F, Van Eyck J, Meyfroidt G (2013) Predictive data mining on monitoring data from the intensive care unit. J Clin Monit Comput 27:449–453CrossRefPubMedGoogle Scholar
  15. 15.
    Flechet M, Guiza F, Schetz M, Wouters P, Vanhorebeek I, Derese I, Gunst J, Spriet I, Casaer M, Van den Berghe G, Meyfroidt G (2017) AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin. Intensive Care Med 43:764–773CrossRefPubMedGoogle Scholar
  16. 16.
    Bhatt DL, Mehta C (2016) Adaptive designs for clinical trials. N Engl J Med 375:65–74CrossRefPubMedGoogle Scholar
  17. 17.
    Pocock SJ, Stone GW (2016) The primary outcome fails—what next? N Engl J Med 375:861–870CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature and ESICM 2017

Authors and Affiliations

  1. 1.HP2 laboratoryUniversity of Grenoble AlpesGrenobleFrance
  2. 2.Department of Physiology and SleepGrenoble Alpes University Hospital (CHU de Grenoble)GrenobleFrance
  3. 3.Department of Intensive Care MedicineUniversity Hospitals LeuvenLouvainBelgium
  4. 4.Laboratory of Intensive Care Medicine, Department of Cellular and Molecular MedicineKU LeuvenLouvainBelgium
  5. 5.Inserm UMR 1137-IAME Team 5-DeSCID : Decision SCiences in Infectious Diseases, control and care INSERM/Paris Diderot, Sorbonne Paris Cité UniversityParisFrance
  6. 6.Medical ICUParis Diderot University/Bichat University Hospital, APHPParisFrance
  7. 7.Inserm, U1042GrenobleFrance

Personalised recommendations