FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data-Study protocol of a multicenter trial.
Dora B BaloghGernot HudelistDmitrijs BļizņuksJayanth RaghothamaChristian M BeckerRoman HoraceHarald KrentelAndrew W HorneNicolas BourdelGabriella MarkiCarla TomassettiUlrik Bak KirkNandor AcsAttila BokorPublished in: PloS one (2024)
We aim to develop a phenotypic description of women with endometriosis by linking the collected data with existing registry-based information on endometriosis diagnosis, healthcare utilization, and big data approach. This may help to achieve earlier detection of endometriosis with pelvic pain and significantly reduce the current diagnostic delay. Additionally, we may identify dietary components that worsen the quality of life and pain in women with endometriosis, upon which we can create real-world data-based nutritional recommendations.
Keyphrases
- big data
- machine learning
- artificial intelligence
- study protocol
- healthcare
- chronic pain
- electronic health record
- randomized controlled trial
- pain management
- clinical trial
- neuropathic pain
- cross sectional
- spinal cord injury
- case report
- open label
- loop mediated isothermal amplification
- quantum dots
- clinical practice
- social media
- sensitive detection