Unsupervised machine learning highlights the challenges of subtyping disorders of gut-brain interaction.

Journal: Neurogastroenterology and motility
PMID: 39119757

Abstract

BACKGROUND: Unsupervised machine learning describes a collection of powerful techniques that seek to identify hidden patterns in unlabeled data. These techniques can be broadly categorized into dimension reduction, which transforms and combines the original set of measurements to simplify data, and cluster analysis, which seeks to group subjects based on some measure of similarity. Unsupervised machine learning can be used to explore alternative subtyping of disorders of gut-brain interaction (DGBI) compared to the existing gastrointestinal symptom-based definitions of Rome IV.

Authors

  • Jarrah M Dowrick
    Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Nicole C Roy
    High-Value Nutrition National Science Challenge, Auckland, New Zealand.
  • Simone Bayer
    High-Value Nutrition National Science Challenge, Auckland, New Zealand.
  • Chris M A Frampton
    High-Value Nutrition National Science Challenge, Auckland, New Zealand.
  • Nicholas J Talley
    High-Value Nutrition National Science Challenge, Auckland, New Zealand.
  • Richard B Gearry
    High-Value Nutrition National Science Challenge, Auckland, New Zealand.
  • Timothy R Angeli-Gordon
    Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.

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