Enhancing and Personalising Endometriosis Care with Causal Machine Learning

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. Bowles is partially supported by the Austrian Funding Council (FWF) under Meitner M 3338-N. Access this chapter Tax calculation will be finalised at checkout Purchases are for personal use only Similar content being viewed by others

Agarwal, 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 Ballweg, 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. Gynaecol. 18(2), 201–218 (2004). https://doi.org/10.1016/j.bpobgyn.2004.01.003 Blass, I., Sahar, T., Shraibman, A., Ofer, D., Rappoport, N., Linial, M.: Revisiting the risk factors for endometriosis: a machine learning approach. J. Personalized Med. 12(7), 1114 (2022). https://doi.org/10.3390/jpm12071114 Burton, C., Iversen, L., Bhattacharya, S., Ayansina, D., Saraswat, L., Sleeman, D.: Pointers to earlier diagnosis of endometriosis: a nested case-control study using primary care electronic health records. Br. J. Gen. Pract. 67(665), e816–e823 (2017). https://doi.org/10.3399/bjgp17x693497 Conroy, I., et al.: Pelvic pain: what are the symptoms and predictors for surgery, endometriosis and endometriosis severity. Aust. N. Z. J. Obstet. Gynaecol. 61(5), 765–772 (2021). https://doi.org/10.1111/ajo.13379 Cox, H., Henderson, L., Andersen, N., Cagliarini, G., Ski, C.: Focus group study of endometriosis: struggle, loss and the medical merry-go-round. Int. J. Nurs. Pract. 9(1), 2–9 (2003). https://doi.org/10.1046/j.1440-172x.2003.00396.x Eskenazi, B., Warner, M.L.: Epidemiology of endometriosis. Obstet. Gynecol. Clin. North Am. 24(2), 235–258 (1997). https://doi.org/10.1016/s0889-8545(05)70302-8 Fauconnier, A., et al.: Early identification of women with endometriosis by means of a simple patient-completed questionnaire screening tool: a diagnostic study. Fertil. Steril. 116(6), 1580–1589 (2021). https://doi.org/10.1016/j.fertnstert.2021.07.1205 Goldstein, A., Cohen, S.: Self-report symptom-based endometriosis prediction using machine learning. Sci. Rep. 13(1), 5499 (2023). https://doi.org/10.1038/s41598-023-32761-8 Gordon, H.G., Mooney, S.S., Conroy, I.C., Grover, S.R.: When pain is not the whole story: presenting symptoms of women with endometriosis. Aust. N. Z. J. Obstet. Gynaecol. 62(3), 434–438 (2022). https://doi.org/10.1111/ajo.13482 Hine, A.A., Kuster Filipe Bowles, J., Webber, T.: The need for a non-invasive technology for endometriosis detection and care. In: Caring is Sharing-Exploiting the Value in Data for Health and Innovation (2023). https://doi.org/10.3233/shti230073 Iroju, O.G., Olaleke, J.O.: A systematic review of natural language processing in healthcare. Int. J. Inf. Technol. Comput. Sci. 8, 44–50 (2015). https://doi.org/10.5815/ijitcs.2015.08.07 Kleczyk, E.J., et al.: Predicting endometriosis onset using machine learning algorithms. BMC Women’s Health (preprint under review) (2020). https://doi.org/10.21203/rs.3.rs-135736/v1 Kruk, M.E., et al.: High-quality health systems in the sustainable development goals era: time for a revolution. Lancet Glob. Health 6(11), e1196–e1252 (2018). https://doi.org/10.1016/s2214-109x(18)30386-3 Malvezzi, H., Marengo, E.B., Podgaec, S., de Azevedo Piccinato, C.: Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology. J. Transl. Med. 18, 1–21 (2020). https://doi.org/10.1186/s12967-020-02471-0 Markham, S.M., Carpenter, S.E., Rock, J.A.: Extrapelvic endometriosis. Obstet. Gynecol. Clin. North Am. 16(1), 193–219 (1989) McKee, M., Wouters, O.J.: The challenges of regulating artificial intelligence in healthcare: comment on “clinical decision support and new regulatory frameworks for medical devices: are we ready for it?-A viewpoint paper”. Int. J. Health Policy Manag. 12 (2023). https://doi.org/10.34172/ijhpm.2022.7261 Nnoaham, K.E., Hummelshoj, L., Kennedy, S.H., Jenkinson, C., Zondervan, K.T.: World endometriosis research foundation women’s health symptom survey consortium. Developing symptom-based predictive models of endometriosis as a clinical screening tool: results from a multicenter study. Fertil. Steril. 98(3), 692–701 (2012). https://doi.org/10.1016/j.fertnstert.2012.04.022 Pearl, J.: Causality. Cambridge University Press, Cambridge (2009). https://doi.org/10.1017/CBO9780511803161 Pergialiotis, V., et al.: The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public Health 164, 1–6 (2018). https://doi.org/10.1016/j.puhe.2018.07.012 Richens, J.G., Lee, C.M., Johri, S.: Improving the accuracy of medical diagnosis with causal machine learning. Nat. Commun. 11(1), 3923 (2020). https://doi.org/10.1038/s41467-020-17419-7 Rogers, P.A., et al.: Priorities for endometriosis research: recommendations from an international consensus workshop. Reprod. Sci. 16, 335–346 (2009). https://doi.org/10.1177/1933719108330568 Rolla, E.: Endometriosis: advances and controversies in classification, pathogenesis, diagnosis, and treatment. F1000Research 8 (2019). https://doi.org/10.12688/f1000research.14817.1 Sankaravadivel, V., Thalavaipillai, S.: Symptoms based endometriosis prediction using machine learning. Bull. Electr. Eng. Inform. 10(6), 3102–3109 (2021). https://doi.org/10.11591/eei.v10i6.3254 Shafrir, A.L., et al.: Risk for and consequences of endometriosis: a critical epidemiologic review. Best Pract. Res. Clin. Obstet. Gynaecol. 51, 1–15 (2018). https://doi.org/10.1016/j.bpobgyn.2018.06.001 Simoens, S., et al.: The burden of endometriosis: costs and quality of life of women with endometriosis and treated in referral centres. Hum. Reprod. 27(5), 1292–1299 (2012). https://doi.org/10.1093/humrep/des073 Sivajohan, 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

We 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. Author information Authors and Affiliations Corresponding author Editor information Editors and Affiliations Rights and permissions Copyright information © 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG About this paper Cite this paper Hine, 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 Download citation DOI: https://doi.org/10.1007/978-3-031-74443-3_1 Published: Publisher Name: Springer, Cham Print ISBN: 978-3-031-74442-6 Online ISBN: 978-3-031-74443-3 eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)Springer Nature Proceedings excluding Computer Science