IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v19y2024i1p1-21.html
   My bibliography  Save this article

Optimizing Cancer Patient Classification Forecasting With Bayesian Pattern Recognition: A Case Study From Thailand Administrative Program

Author

Listed:
  • Praowpan Tansitpong

    (NIDA Business School, National Institute of Development Administration, Thailand)

Abstract

This research examines patterns in cancer treatment by analyzing electronic medical record (EMR) data, with the goal of optimizing healthcare provision and improving patient outcomes. The study aims to apply Bayesian prediction models and regression analysis to determine the posterior probability of comorbidities and forecast patient arrivals. The implemented algorithms allow for the customization of treatment techniques, resulting in enhanced effectiveness of therapy and improved decision-making in healthcare delivery. Utilizing Bayesian approaches to analyze EMR data provides insights into the intricacies of cancer treatment and related expenses. The application of this study could be useful to enhance healthcare information systems and informatics by using data-driven insights to improve cancer care practices and operational efficiency in hospital settings.

Suggested Citation

  • Praowpan Tansitpong, 2024. "Optimizing Cancer Patient Classification Forecasting With Bayesian Pattern Recognition: A Case Study From Thailand Administrative Program," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 19(1), pages 1-21, January.
    • Handle: RePEc:igg:jhisi0:v:19:y:2024:i:1:p:1-21
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.351244
    Download Restriction: no
    ---><---

    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:19:y:2024:i:1:p:1-21. 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: 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.