Presenting a prediction model for HELLP syndrome through data mining.

Journal: BMC medical informatics and decision making
PMID: 40098129

Abstract

BACKGROUND: The HELLP syndrome represents three complications: hemolysis, elevated liver enzymes, and low platelet count. Since the causes and pathogenesis of HELLP syndrome are not yet fully known and well understood, distinguishing it from other pregnancy-related disorders is complicated. Furthermore, late diagnosis leads to a delay in treatment, which challenges disease management. The present study aimed to present a machine learning (ML) attitude for diagnosing HELLP syndrome based on non-invasive parameters.

Authors

  • Boshra Farajollahi
    Department of Health Information Management, School of Health Management and Information Sciences, University of Medical Sciences, Tehran, Iran.
  • Mohammadjavad Sayadi
    Department of Computer Engineering, University of Applied Science and Technology (UAST), Tehran, Iran. mjsayadi@nus.ac.ir.
  • Mostafa Langarizadeh
    Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
  • Ladan Ajori
    Department of Obstetrics and Gynecology, Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tajrish Sq, Tehran, Iran.

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