@inproceedings{71593710f933489f811fa85143eff2c0,
title = "Enhancing and Personalising Endometriosis Care with Causal Machine Learning",
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.",
keywords = "Artificial Intelligence, Diagnosis, Endometriosis, Female reproductive health, Menstrual health, Prediction models",
author = "Ariane Hine and Thais Webber and Bowles, {Juliana K. F.}",
year = "2024",
month = dec,
day = "20",
doi = "10.1007/978-3-031-74443-3_1",
language = "English",
isbn = "9783031744426",
volume = "884",
series = "Lecture Notes in Networks and Systems (LNNS)",
pages = "3--25",
editor = "Nitin Naik and Paul Jenkins and Shaligram Prajapat and Paul Grace",
booktitle = "Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024",
}