{"paper_id":"4be8fa5a-2ac4-4b35-bf00-3557381ac736","body_text":"Abstract\nEndometriosis 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.\nBowles is partially supported by the Austrian Funding Council (FWF) under Meitner M 3338-N.\nAccess this chapter\nTax calculation will be finalised at checkout\nPurchases are for personal use only\nSimilar content being viewed by others\nReferences\nAgarwal, S.K., et al.: Clinical diagnosis of endometriosis: a call to action. Am. J. Obstet. Gynecol. 220(4), 354-e1 (2019). https://doi.org/10.1016/j.ajog.2018.12.039\nBallweg, M.L.: Impact of endometriosis on women’s health: comparative historical data show that the earlier the onset, the more severe the disease. Best Pract. Res. Clin. Obstet. 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Reprod. 27(5), 1292–1299 (2012). https://doi.org/10.1093/humrep/des073\nSivajohan, B., Elgendi, M., Menon, C., Allaire, C., Yong, P., Bedaiwy, M.A.: Clinical use of artificial intelligence in endometriosis: a scoping review. NPJ Digit. Med. 5(1), 109 (2022). https://doi.org/10.1038/s41746-022-00638-1\nAcknowledgement\nWe thank Thomas Ebner from the Kinderwunsch Zentrum, Kepler Universitätsklinikum, Linz, for valuable insights into the processes in the Austrian healthcare system. We thank the team from the School of Nursing, University of São Paulo, Brazil, led by Lislaine Aparecida Fracolli with Ana Luiza Vilela Borges, Carla Marins Silva and Marlise de Oliveira Pimentel Lima, for ongoing discussions on the Brazilian perspective of endometriosis care.\nAuthor information\nAuthors and Affiliations\nCorresponding author\nEditor information\nEditors and Affiliations\nRights and permissions\nCopyright information\n© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG\nAbout this paper\nCite this paper\nHine, A., Webber, T., Bowles, J. (2024). Enhancing and Personalising Endometriosis Care with Causal Machine Learning. In: Naik, N., Jenkins, P., Prajapat, S., Grace, P. (eds) Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI, July 3–4, 2024, London, UK. C3AI 2024. Lecture Notes in Networks and Systems, vol 884. Springer, Cham. https://doi.org/10.1007/978-3-031-74443-3_1\nDownload citation\nDOI: https://doi.org/10.1007/978-3-031-74443-3_1\nPublished:\nPublisher Name: Springer, Cham\nPrint ISBN: 978-3-031-74442-6\nOnline ISBN: 978-3-031-74443-3\neBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)Springer Nature Proceedings excluding Computer Science","source_license":"CC0","license_restricted":false}