Re-investigation of functional gastrointestinal disorders utilizing a machine learning approach.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Functional gastrointestinal disorders (FGIDs), as a group of syndromes with no identified structural or pathophysiological biomarkers, are currently classified by Rome criteria based on gastrointestinal symptoms (GI). However, the high overlap among FGIDs in patients makes treatment and identifying underlying mechanisms challenging. Furthermore, disregarding psychological factors in the current classification, despite their approved relationship with GI symptoms, underlines the necessity of more investigation into grouping FGID patients. We aimed to provide more homogenous and well-separated clusters based on both GI and psychological characteristics for patients with FGIDs using an unsupervised machine learning algorithm.

Authors

  • Elahe Mousavi
    Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Ammar Hasanzadeh Keshteli
    Department of Bioinformatics, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Hezar Jerib Street, Po Box 8174673461, Isfahan, Iran.
  • Mohammadreza Sehhati
    Department of Bioinformatics, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. mr.sehhati@amt.mui.ac.ir.
  • Ahmad Vaez
    Department of Bioinformatics, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Hezar Jerib Street, Po Box 8174673461, Isfahan, Iran.
  • Peyman Adibi
    Integrative Functional Gastroenterology and Hepatology Research Center, Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.