ArticleJournal of personalized medicine2022
Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Interpretable machine learning for endometriosis classification: a rule-based approach.BMC medical informatics and decision making · 2026Article
- InterFeat: a pipeline for finding interesting scientific features.Scientific reports · 2026Article
- Article
- Genetic and Epigenetic Components in the Pathogenesis of Adenomyosis and Endometriosis in Adolescents.Biomedicines · 2025Review
- Study on Chinese patients' views on endometriosis surgery and fertility preservation in the context of declining fertility.BMC women's health · 2025Article
- Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset.World journal of clinical cases · 2025Article
- UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data.Nature communications · 2025Article
- Artificial Intelligence in Assisted Reproductive Technology: A New Era in Fertility Treatment.Cureus · 2025Review
- Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review.JNMA; journal of the Nepal Medical Association · 2025Review
- Machine Learning-Based Detection of Endometriosis: A Retrospective Study in A Population of Iranian Female Patients.International journal of fertility & sterility · 2024Article
- Application of machine learning techniques in the diagnosis of endometriosis.BMC women's health · 2024Article
- Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans?Journal of clinical medicine · 2024Review
- Is there an association between endometriosis and subsequent breast cancer? A retrospective cohort study from Germany.Breast cancer research and treatment · 2024Observational
- The causality between gut microbiota and endometriosis: a bidirectional Mendelian randomization study.Frontiers in medicine · 2024Article
- Polygenic risk score phenome-wide association study reveals an association between endometriosis and testosterone.BMC medicine · 2023Article
- Article
- Evaluating the risk of endometriosis based on patients' self-assessment questionnaires.Reproductive biology and endocrinology : RB&E · 2023Article
- Genomic characterisation of the overlap of endometriosis with 76 comorbidities identifies pleiotropic and causal mechanisms underlying disease risk.Human genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
Endometriosis is a condition characterized by implants of endometrial tissues into extrauterine sites, mostly within the pelvic peritoneum. The prevalence of endometriosis is under-diagnosed and is estimated to account for 5-10% of all women of reproductive age. The goal of this study was to develop a model for endometriosis based on the UK-biobank (UKB) and re-assess the contribution of known risk factors to endometriosis. We partitioned the data into those diagnosed with endometriosis (5924; ICD-10: N80) and a control group (142,723). We included over 1000 variables from the UKB covering personal information about female health, lifestyle, self-reported data, genetic variants, and medical history prior to endometriosis diagnosis. We applied machine learning algorithms to train an endometriosis prediction model. The optimal prediction was achieved with the gradient boosting algorithms of CatBoost for the data-combined model with an area under the ROC curve (ROC-AUC) of 0.81. The same results were obtained for women from a mixed ethnicity population of the UKB (7112; ICD-10: N80). We discovered that, prior to being diagnosed with endometriosis, affected women had significantly more ICD-10 diagnoses than the average unaffected woman. We used SHAP, an explainable AI tool, to estimate the marginal impact of a feature, given all other features. The informative features ranked by SHAP values included irritable bowel syndrome (IBS) and the length of the menstrual cycle. We conclude that the rich population-based retrospective data from the UKB are valuable for developing unified machine learning endometriosis models despite the limitations of missing data, noisy medical input, and participant age. The informative features of the model may improve clinical utility for endometriosis diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.