Enhancing and Personalising Endometriosis Care with Causal Machine Learning

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

doi:10.1007/978-3-031-74443-3_1

Enhancing and Personalising Endometriosis Care with Causal Machine Learning

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

University of St. Andrews

Research output: Chapter in Book/Published conference output › Conference 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 language	English
Title of host publication	Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024
Editors	Nitin Naik, Paul Jenkins, Shaligram Prajapat, Paul Grace
Pages	3-25
Number of pages	23
Volume	884
ISBN (Electronic)	9783031744433
DOIs	https://doi.org/10.1007/978-3-031-74443-3_1
Publication status	Published - 20 Dec 2024

Publication series

Name	Lecture Notes in Networks and Systems (LNNS)
Volume	884

Bibliographical note

Copyright © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
This is an accepted manuscript of a Proceedings paper, published in: 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

Funding

Bowles is partially supported by the Austrian Funding Council (FWF) under Meitner M 3338-N.

Keywords

Artificial Intelligence
Diagnosis
Endometriosis
Female reproductive health
Menstrual health
Prediction models

Access to Document

10.1007/978-3-031-74443-3_1

TheC3AI_34_AI-Causal ML-Endometriosis
Copyright © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an accepted manuscript of a Proceedings paper, published in: 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
Accepted author manuscript, 1.94 MBLicence: Unspecified

Cite this

Hine, A., Webber, T., & Bowles, J. K. F. (2024). Enhancing and Personalising Endometriosis Care with Causal Machine Learning. In N. Naik, P. Jenkins, S. Prajapat, & P. Grace (Eds.), Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024 (Vol. 884, pp. 3-25). (Lecture Notes in Networks and Systems (LNNS); Vol. 884). https://doi.org/10.1007/978-3-031-74443-3_1

Hine, Ariane ; Webber, Thais ; Bowles, Juliana K. F. . / Enhancing and Personalising Endometriosis Care with Causal Machine Learning. Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024. editor / Nitin Naik ; Paul Jenkins ; Shaligram Prajapat ; Paul Grace. Vol. 884 2024. pp. 3-25 (Lecture Notes in Networks and Systems (LNNS)).

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Hine, A, Webber, T & Bowles, JKF 2024, Enhancing and Personalising Endometriosis Care with Causal Machine Learning. in N Naik, P Jenkins, S Prajapat & P Grace (eds), Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024. vol. 884, Lecture Notes in Networks and Systems (LNNS), vol. 884, pp. 3-25. https://doi.org/10.1007/978-3-031-74443-3_1

Enhancing and Personalising Endometriosis Care with Causal Machine Learning. / Hine, Ariane; Webber, Thais; Bowles, Juliana K. F. .
Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024. ed. / Nitin Naik; Paul Jenkins; Shaligram Prajapat; Paul Grace. Vol. 884 2024. p. 3-25 (Lecture Notes in Networks and Systems (LNNS); Vol. 884).

Research output: Chapter in Book/Published conference output › Conference publication

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Hine A, Webber T, Bowles JKF. Enhancing and Personalising Endometriosis Care with Causal Machine Learning. In Naik N, Jenkins P, Prajapat S, Grace P, editors, Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024. Vol. 884. 2024. p. 3-25. (Lecture Notes in Networks and Systems (LNNS)). doi: 10.1007/978-3-031-74443-3_1

Enhancing and Personalising Endometriosis Care with Causal Machine Learning

Abstract

Publication series

Bibliographical note

Funding

Keywords

Access to Document

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