IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v14y2019i3p40-57.html
   My bibliography  Save this article

Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

Author

Listed:
  • Yazan Alnsour

    (University of Illinois at Springfield, Springfield, USA)

  • Rassule Hadidi

    (University of Illinois at Springfield, Springfield, USA)

  • Neetu Singh

    (University of Illinois at Springfield, Springfield, USA)

Abstract

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.

Suggested Citation

  • Yazan Alnsour & Rassule Hadidi & Neetu Singh, 2019. "Using Data Analytics to Predict Hospital Mortality in Sepsis Patients," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 14(3), pages 40-57, July.
    • Handle: RePEc:igg:jhisi0:v:14:y:2019:i:3:p:40-57
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.2019070104
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manel Saad Saoud & Abdelhak Boubetra & Safa Attia, 2016. "A Simulation Knowledge Extraction-based Decision Support System for the Healthcare Emergency Department," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 11(2), pages 19-37, April.
    2. Steven Walczak & Senanu R. Okuboyejo, 2017. "An Artificial Neural Network Classification of Prescription Nonadherence," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 12(1), pages 1-13, January.
    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. Miguel Angel Ortíz-Barrios & Juan-José Alfaro-Saíz, 2020. "Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review," IJERPH, MDPI, vol. 17(8), pages 1-41, April.

    More about this item

    Statistics

    Access and download statistics

    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:igg:jhisi0:v:14:y:2019:i:3:p:40-57. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.