IDEAS home Printed from https://ideas.repec.org/a/adp/jbboaj/v1y2017i3p63-64.html
   My bibliography  Save this article

Big Data has a Big Role in Biostatistics with Big Challenges and Big Expectations

Author

Listed:
  • Sergio Davalos

    (Center for Business Analytics, University of Washington, USA)

Abstract

Big data has now gone beyond a conceptual construct to a viable working approach for addressing and making use of huge volumes of data. The expectations of big data applications and outcomes in healthcare such as: a 20% decrease in patient mortality, better information regarding patient health and symptoms, reducing readmission, better point of care decision making, integration of smart devices and sensors with data bases, everyday genome sequencing, developing a treatment approach for cancer, and assessing the risk of readmission [1-3].

Suggested Citation

  • Sergio Davalos, 2017. "Big Data has a Big Role in Biostatistics with Big Challenges and Big Expectations," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 1(3), pages 63-64, May.
    • Handle: RePEc:adp:jbboaj:v:1:y:2017:i:3:p:63-64
    DOI: 10.19080/BBOAJ.2017.01.555563
    as

    Download full text from publisher

    File URL: https://juniperpublishers.com/bboaj/pdf/BBOAJ.MS.ID.555563.pdf
    Download Restriction: no
    File URL: https://juniperpublishers.com/bboaj/BBOAJ.MS.ID.555563.php
    Download Restriction: no
    File URL: https://libkey.io/10.19080/BBOAJ.2017.01.555563?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jan Pablo Burgard & Joscha Krause & Dennis Kreber & Domingo Morales, 2021. "The generalized equivalence of regularization and min–max robustification in linear mixed models," Statistical Papers, Springer, vol. 62(6), pages 2857-2883, December.
    2. Jan Pablo Burgard & Carina Moreira Costa & Martin Schmidt, 2024. "Robustification of the k-means clustering problem and tailored decomposition methods: when more conservative means more accurate," Annals of Operations Research, Springer, vol. 339(3), pages 1525-1568, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:adp:jbboaj:v:1:y:2017:i:3:p:63-64. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Robert Thomas (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.