Abstract
Endometriosis is a complex, poorly understood, female health condition that can markedly reduce a woman's quality of life. The gold-standard diagnostic method for Endometriosis is invasive laparoscopic surgery, which is costly, not timely, and comes with risks to the patient. We argue that the need for a non-invasive diagnosis procedure, higher quality of patient care and reduced diagnosis delay, can be fulfilled by advances and research to devise innovative computational solutions. To leverage computational and algorithmic techniques, enhanced data recording and sharing are vital. We discuss the potential benefits of using personalised computational healthcare on both the clinician and patient side, reducing the lengthy average diagnosis time (currently around 8 years).
Original language | English |
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Title of host publication | Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023 |
Editors | Maria Hagglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindskold, Parisis Gallos |
Pages | 103-107 |
Number of pages | 5 |
Volume | 302 |
ISBN (Electronic) | 978-1-64368-389-8 |
DOIs | |
Publication status | Published - 18 May 2023 |
Event | 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023 - Gothenburg, Sweden Duration: 22 May 2023 → 25 May 2023 |
Publication series
Name | Studies in Health Technology and Informatics |
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Volume | 302 |
ISSN (Print) | 0926-9630 |
ISSN (Electronic) | 1879-8365 |
Conference
Conference | 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023 |
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Country/Territory | Sweden |
City | Gothenburg |
Period | 22/05/23 → 25/05/23 |
Bibliographical note
© 2023 European Federation for Medical Informatics (EFMI) and IOS Press.This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Keywords
- Artificial Intelligence
- Diagnosis time
- Endometriosis
- Female reproductive health
- Menstrual health
- Predictions models