Revisiting the subtyping of bowel-based disorders using unsupervised machine learning - presented by Dr. Jarrah Dowrick

Revisiting the subtyping of bowel-based disorders using unsupervised machine learning

Dr. Jarrah Dowrick

Dr. Jarrah Dowrick
Revisiting the subtyping of bowel-based disorders using unsupervised machine learning
Dr. Jarrah Dowrick
Jarrah Dowrick
University of Auckland

The Rome IV criteria, a widely used symptom-based diagnostic tool for disorders of gut-brain interaction (DGBI), have enabled clinicians to provide diagnoses for patients whose gastrointestinal (GI) symptoms lack organic explanations. However, challenges remain in identifying robust diagnostic biomarkers, predicting treatment outcomes, and ensuring diagnostic stability. Unsupervised machine learning can be used to step beyond existing clinical definitions to uncover intrinsic patient subtypes possibly unburdened by these limitations.

In this talk, Jarrah will provide an overview of bowel-based DGBI subtyping, present the findings from his recent application of unsupervised machine learning to revisit this problem, and introduce his next steps in improving DGBI diagnosis and treatment strategies.

References
  • 1.
    J. M. Dowrick et al. (2024) Unsupervised machine learning highlights the challenges of subtyping disorders of gut‐brain interaction. Neurogastroenterology & Motility
  • 2.
    J. M. Dowrick et al. (2024) Tu1684 REVISITING THE SUBTYPING OF LOWER GASTROINTESTINAL DISORDERS OF GUT-BRAIN INTERACTION PATIENTS USING UNSUPERVISED MACHINE LEARNING. Gastroenterology
  • 3.
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12 Labours Seminar Series
Auckland Bioengineering Institute (University of Auckland)
Cite as
J. Dowrick (2024, September 4), Revisiting the subtyping of bowel-based disorders using unsupervised machine learning
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Listed seminar This seminar is open to all
Recorded Available to all
Video length 35:33
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