Enhancing and Personalising Endometriosis Care with Causal Machine Learning

Ariane Hine, Thais Webber, Juliana K. F. Bowles

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Endometriosis poses significant challenges in diagnosis and management due to the wide range of varied symptoms and systemic implications. Integrating machine learning into healthcare screening processes can significantly enhance and optimise resource allocation and diagnostic efficiency, and facilitate more tailored and personalised treatment plans. This paper discusses the potential of leveraging patient-reported symptom data through causal machine learning to advance endometriosis care and reduce the lengthy diagnostic delays associated with this condition. The goal is to propose a novel personalised non-invasive diagnostic approach that understands the underlying causes of patient symptoms and combines health records and other factors to enhance prediction accuracy, providing an approach that can be utilised globally.
Original languageEnglish
Title of host publicationContributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024
EditorsNitin Naik, Paul Jenkins, Shaligram Prajapat, Paul Grace
Pages3-25
Number of pages23
Volume884
ISBN (Electronic)9783031744433
DOIs
Publication statusPublished - 20 Dec 2024

Publication series

NameLecture Notes in Networks and Systems (LNNS)
Volume884

Keywords

  • Artificial Intelligence
  • Diagnosis
  • Endometriosis
  • Female reproductive health
  • Menstrual health
  • Prediction models

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