Enhancing and personalising endometriosis care with causal machine learning

Ariane Alice Hine; Thais Webber; Juliana Kuster Filipe Bowles

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

Enhancing and personalising endometriosis care with causal machine learning

Ariane Alice Hine^*, Thais Webber, Juliana Kuster Filipe Bowles

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 & AI, July 3–4, 2024, London, UK
Subtitle of host publication	The C3AI 2024
Editors	Nitin Naik, Paul Jenkins, Shaligram Prajapat, Paul Grace
Place of Publication	Cham
Publisher	Springer
Pages	3-25
Number of pages	23
ISBN (Electronic)	9783031744433
ISBN (Print)	9783031744426
DOIs	https://doi.org/10.1007/978-3-031-74443-3_1
Publication status	E-pub ahead of print - 20 Dec 2024
Event	The International Conference on Computing, Communication, Cybersecurity & AI (The C3AI) - London, United Kingdom Duration: 3 Jul 2024 → 4 Jul 2024 https://www.thec3ai.com/

Publication series

Name	Lecture notes in networks and systems
Publisher	Springer
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	The International Conference on Computing, Communication, Cybersecurity & AI (The C3AI)
Abbreviated title	The C3AI
Country/Territory	United Kingdom
City	London
Period	3/07/24 → 4/07/24
Internet address	https://www.thec3ai.com/

Keywords

Female reproductive health
Endometriosis
Artificial intelligence
Prediction models
Diagnosis
Menstrual health

Access to Document

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

Hine-2024-Enhancing-and-personalising-C3AI-AAM-CCBY
Copyright © 2024 the Authors. This work has been made available online in accordance with the Rights Retention Strategy. This accepted manuscript is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The final published version of this work is available at https://doi.org/10.1007/978-3-031-74443-3_1.
Accepted author manuscript, 2.11 MB

Cite this

Hine, A. A., Webber, T., & Kuster Filipe Bowles, J. (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 & AI, July 3–4, 2024, London, UK: The C3AI 2024 (pp. 3-25). (Lecture notes in networks and systems). Springer. Advance online publication. https://doi.org/10.1007/978-3-031-74443-3_1

Hine, Ariane Alice ; Webber, Thais ; Kuster Filipe Bowles, Juliana. / Enhancing and personalising endometriosis care with causal machine learning. Contributions presented at The international conference on computing, communication, cybersecurity & AI, July 3–4, 2024, London, UK: The C3AI 2024. editor / Nitin Naik ; Paul Jenkins ; Shaligram Prajapat ; Paul Grace. Cham : Springer, 2024. pp. 3-25 (Lecture notes in networks and systems).

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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 = "Female reproductive health, Endometriosis, Artificial intelligence, Prediction models, Diagnosis, Menstrual health",

author = "Hine, {Ariane Alice} and Thais Webber and {Kuster Filipe Bowles}, Juliana",

note = "Funding: Bowles is partially supported by the Austrian Funding Council (FWF) under Meitner M 3338-N.; The International Conference on Computing, Communication, Cybersecurity & AI (The C3AI), The C3AI ; Conference date: 03-07-2024 Through 04-07-2024",

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language = "English",

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Hine, AA, Webber, T & Kuster Filipe Bowles, J 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 & AI, July 3–4, 2024, London, UK: The C3AI 2024. Lecture notes in networks and systems, Springer, Cham, pp. 3-25, The International Conference on Computing, Communication, Cybersecurity & AI (The C3AI), London, United Kingdom, 3/07/24. https://doi.org/10.1007/978-3-031-74443-3_1

Enhancing and personalising endometriosis care with causal machine learning. / Hine, Ariane Alice; Webber, Thais; Kuster Filipe Bowles, Juliana.
Contributions presented at The international conference on computing, communication, cybersecurity & AI, July 3–4, 2024, London, UK: The C3AI 2024. ed. / Nitin Naik; Paul Jenkins; Shaligram Prajapat; Paul Grace. Cham: Springer, 2024. p. 3-25 (Lecture notes in networks and systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Enhancing and personalising endometriosis care with causal machine learning

AU - Hine, Ariane Alice

AU - Webber, Thais

AU - Kuster Filipe Bowles, Juliana

N1 - Funding: Bowles is partially supported by the Austrian Funding Council (FWF) under Meitner M 3338-N.

PY - 2024/12/20

Y1 - 2024/12/20

N2 - 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.

AB - 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.

KW - Female reproductive health

KW - Endometriosis

KW - Artificial intelligence

KW - Prediction models

KW - Diagnosis

KW - Menstrual health

U2 - 10.1007/978-3-031-74443-3_1

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

M3 - Conference contribution

SN - 9783031744426

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BT - Contributions presented at The international conference on computing, communication, cybersecurity & AI, July 3–4, 2024, London, UK

A2 - Naik, Nitin

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A2 - Prajapat, Shaligram

A2 - Grace, Paul

PB - Springer

CY - Cham

T2 - The International Conference on Computing, Communication, Cybersecurity & AI (The C3AI)

Y2 - 3 July 2024 through 4 July 2024

ER -

Hine AA, Webber T, Kuster Filipe Bowles J. 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 & AI, July 3–4, 2024, London, UK: The C3AI 2024. Cham: Springer. 2024. p. 3-25. (Lecture notes in networks and systems). Epub 2024 Dec 20. doi: 10.1007/978-3-031-74443-3_1

Enhancing and personalising endometriosis care with causal machine learning

Abstract

Publication series

Conference

Keywords

Access to Document

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Cite this