IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v21y2019i3d10.1007_s11009-019-09716-6.html
   My bibliography  Save this article

Real-Time Change-Point Detection Algorithm with an Application to Glycemic Control for Diabetic Pregnant Women

Author

Listed:
  • Michal Shauly-Aharonov

    (The Hebrew University of Jerusalem)

  • Orit Barenholz-Goultschin

    (Shaare Zedek Medical Center)

Abstract

Glycemic control in pregnancies of diabetic women is still suboptimal; birth defects and late miscarriages (i.e., second trimester miscarriages) are much more common in diabetic pregnancies than in the general population. This paper presents a pilot study for real-time detection of dangerous changes in glucose level, namely such that are associated with birth defects or miscarriage during the first trimester of pregnancy. Its main goals are to present an algorithm and to verify that it has practical potential to notify early enough of an increased risk of adverse outcomes in diabetic pregnancies. The study included eight women with type 1 diabetes who wore a Continuous Glucose Monitor (CGM; a device that reads and transmits the glucose level every five minutes) during the entire first trimester. Nonparametric change-point detection methods were applied on CGM data; results show evidence that an increase in glucose variability is associated with heightened risk for late miscarriage, and that this change could have been detected early enough to reduce fluctuations. By contrast, standard indicators for glycemic control in pregnancy failed to identify this peril.

Suggested Citation

  • Michal Shauly-Aharonov & Orit Barenholz-Goultschin, 2019. "Real-Time Change-Point Detection Algorithm with an Application to Glycemic Control for Diabetic Pregnant Women," Methodology and Computing in Applied Probability, Springer, vol. 21(3), pages 931-944, September.
  • Handle: RePEc:spr:metcap:v:21:y:2019:i:3:d:10.1007_s11009-019-09716-6
    DOI: 10.1007/s11009-019-09716-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11009-019-09716-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11009-019-09716-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aleksey S. Polunchenko & Alexander G. Tartakovsky, 2012. "State-of-the-Art in Sequential Change-Point Detection," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 649-684, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Savas Dayanik & Semih O Sezer, 2023. "Model Misspecification in Discrete Time Bayesian Online Change Detection," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-27, March.
    2. Krawiec, Michał & Palmowski, Zbigniew & Płociniczak, Łukasz, 2018. "Quickest drift change detection in Lévy-type force of mortality model," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 432-450.
    3. Aleksey S. Polunchenko & Grigory Sokolov, 2016. "An Analytic Expression for the Distribution of the Generalized Shiryaev–Roberts Diffusion," Methodology and Computing in Applied Probability, Springer, vol. 18(4), pages 1153-1195, December.

    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:spr:metcap:v:21:y:2019:i:3:d:10.1007_s11009-019-09716-6. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.