[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119247-en":3,"doc-seo-119247-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119247,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Enhancing and Personalising Endometriosis Care with Causal Machine Learning - 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 can enhance and optimise resource allocation and diagnostic efficiency, enabling more tailored and personalised treatment plans. The work leverages patient-reported symptom data through causal machine learning to reduce lengthy diagnostic delays, aiming to build a novel personalised non-invasive diagnostic approach that identifies underlying causes and improves prediction accuracy for global use.","Enhancing and Personalising Endometriosis Care  \nwith Causal Machine Learning ⋆  \nAriane Hine 1[0000−0002−7447−9371], Thais Webber2[0000−0002−8091−6021], and Juliana Bowles 1 ,3[0000−0002−5918−9114]  \n1 School of Computer Science, University of St Andrews, KY16 9SX St Andrews, UK {aah4,[jkfb}@st-andrews.ac.uk](jkfb}@st-andrews.ac.uk)  \n2 School of Computer Science and Digital Tech. , Aston University, B4 7ET, UK [t.webber@aston.ac.uk](t.webber@aston.ac.uk)  \n3 Software Competence Centre Hagenberg, Softwarepark, 4232 Hagenberg, Austria [juliana.bowles@scch.at](juliana.bowles@scch.at)  \nAbstract. 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 patientreported 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.  \nKeywords: Female reproductive health · Endometriosis · Artificial Intelligence · Prediction models · Diagnosis · Menstrual health  \n1 Introduction  \nEndometriosis is a prevalent, chronic, inflammatory condition affecting approximately 10% of individuals assigned female at birth during their reproductive years and beyond. Historically characterised as a gynaecological disease [7], recent research has revealed its systemic implications [16] . Endometriosis can affect virtually all organs in the human body, with symptoms ranging from asymptomatic cases to severe, life-altering conditions [14, 26] .  \nCommon symptoms include menstrual irregularities, heavy menstrual flow (menorrhagia), painful periods (dysmenorrhea), pain during sexual intercourse (dyspareunia), chronic pelvic pain unrelated to menstruation, tenderness, adnexal mass (growths near the uterus/ovaries), infertility or subfertility, depression, anxiety, abdominal bloating, nausea, and restricted mobility [10, 26] . In  \n⋆ Bowles is partially supported by the Austrian Funding Council (FWF) under Meitner M 3338-N.  \nA. Hine et al.  \nmore advanced cases, patients often experience bowel and bladder-related symptoms such as painful bowel movements (dyschezia), loss of bladder control (dysuria), blood in the urine or stool during menstruation, painful urination, and chronic fatigue [24] . Given the multitude of symptoms, overlapping conditions, and the complexity of the disease, the root cause of endometriosis has not yet been conclusively determined [1, 10] . It has been hypothesised that one cause may be retrograde menstruation, where menstrual blood flows backwards into the pelvis during menstruation. Some of the menstrual blood contains endometrial tissue, which is believed to implant within a woman’s abdomen, leading to patches of endometriosis [18] . This is not the sole cause of the condition, however, as 90% of all menstruating women are thought to experience this phenomenon. On the contrary, other sources suggest that endometriosis may start at birth, with symptoms not triggering until puberty [23] . There are many varying theories on the source of the condition, with no one definitive cause. The aetiology of endometriosis remains medically undetermined, complicated by its complex multifactorial nature. This complexity likely contributes to the challenges in understanding and diagnosing the condition, as multiple variables seem to influence its development and progression in patients [4, 15] .  \nFurther complexity arises when patients with endometriosis, w","cbCaime5WugsEnhk","https://ap.wps.com/l/cbCaime5WugsEnhk","pdf",2208006,1,23,"English","en",105,"# Introduction\n## Prevalence and symptom complexity\n## Challenges from comorbidities\n## Motivation for causal machine learning\n## Aim of the EndoML Project","[{\"question\":\"What is the goal of the EndoML Project?\",\"answer\":\"The EndoML Project aims to develop a personalised, non-invasive diagnostic approach that uses diverse health data, including patient-reported information, to reduce diagnostic delays. By understanding underlying causes of symptoms, it targets improved prediction accuracy and actionable diagnostic insights.\"}]","Enhancing and Personalising Endometriosis Care with Causal Machine Learning - Abstract | PDF",1785723295,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"enhancing-and-personalising-endometriosis-care-with-causal-machine-learning-abstract","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-and-personalising-endometriosis-care-with-causal-machine-learning-abstract/119247/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of the EndoML Project?","Question",{"text":75,"@type":76},"The EndoML Project aims to develop a personalised, non-invasive diagnostic approach that uses diverse health data, including patient-reported information, to reduce diagnostic delays. 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