[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125615-en":3,"doc-seo-125615-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125615,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Predicting a diagnosis of ankylosing spondylitis using primary care health records - A machine learning approach","Ankylosing spondylitis is a leading inflammatory arthritis cause, yet diagnosis can take many years to confirm. This study used machine learning on Secure Anonymised Information Linkage primary care records to learn profiles associated with future AS diagnoses. Cases were matched to controls without AS or axial spondyloarthritis, with analyses performed separately for men and women. Decision-tree models were built using feature/variable selection and principal component analysis, then validated on test data, showing good performance in test datasets but reduced positive predictive value in general populations with very low prevalence.","PLOS ONE  \nOPEN ACCESS  \nCitation: Kennedy J, Kennedy N, Cooksey R, Choy E, Siebert S, Rahman M, et al. (2023) Predicting a diagnosis of ankylosing spondylitis using primary care health records–A machine learning approach. PLoS ONE 18(3): e0279076 . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1371/journal.pone](10.1371/journal.pone).0279076  \nEditor: Alfredo Vellido, Universitat Politecnica de Catalunya, SPAIN  \nReceived: November 23, 2020  \nAccepted: December 1, 2022  \nPublished: March 31, 2023  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone.0279076](https://doi.org/10.1371/journal.pone.0279076)  \n[Copyright:](Copyright:) © [2023 Kennedy et al](2023 Kennedy et al). This is an open access article 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.  \nData Availability Statement: The data is owned by the SAIL Databank ([https://saildatabank.com/](https://saildatabank.com/)) and all datasets are available from them upon request and appropriate information governance. The  \nRESEARCH ARTICLE  \nPredicting a diagnosis of ankylosing spondylitis using primary care health records– A machine learning approach  \nJonathan Kennedy1 *, Natasha Kennedy1, Roxanne Cooksey1, Ernest Choy2, Stefan Siebert3, Muhammad Rahman1, Sinead Brophy1  \n1 Data Science Building, Swansea University, Wales, United Kingdom, 2 CREATE Centre, Section of Rheumatology, Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom, 3 Institute of Infection Immunity & Inflammation, University of Glasgow, Glasgow, United Kingdom  \n* [j.i.kennedy@swansea.ac.uk](j.i.kennedy@swansea.ac.uk)  \nAbstract  \nAnkylosing spondylitis is the second most common cause of inflammatory arthritis. However, a successful diagnosis can take a decade to confirm from symptom onset (via x-rays) . The aim of this study was to use machine learning methods to develop a profile of the characteristics of people who are likely to be given a diagnosis of AS in future. The Secure Anonymised Information Linkage databank was used. Patients with ankylosing spondylitis were identified using their routine data and matched with controls who had no record of a diagnosis of ankylosing spondylitis or axial spondyloarthritis. Data was analysed separately for men and women. The model was developed using feature/variable selection and principal component analysis to develop decision trees. The decision tree with the highest average F value was selected and validated with a test dataset. The model for men indicated that lower back pain, uveitis, and NSAID use under age 20 is associated with AS development. The model for women showed an older age of symptom presentation compared to men with back pain and multiple pain relief medications. The models showed good prediction (positive predictive value 70%-80%) in test data but in the general population where prevalence is very low (0 .09% of the population in this dataset) the positive predictive value would be very low (0 .33%-0 .25%) . Machine learning can be used to help profile and understand the characteristics of people who will develop AS, and in test datasets with artificially high prevalence, will perform well. However, when applied to a general population with low prevalence rates, such as that in primary care, the positive predictive value for even the best model would be 1.4% . Multiple models may be needed to narrow down the population over time to improve the predictive value and therefore reduce the time to diagnosis of ankylosing spondylitis.  \nIntroduction  \nAnkylosing Spondyl","cbCaidARWSv00Oww","https://ap.wps.com/l/cbCaidARWSv00Oww","pdf",1001618,1,16,"English","en",105,"# Abstract\n## Introduction\n## Methods (machine learning model development)\n## Results (men and women risk profiles)\n## Discussion (prevalence impact on predictive value)","[{\"question\":\"What data source and study design were used to predict ankylosing spondylitis diagnoses?\",\"answer\":\"The study used the Secure Anonymised Information Linkage databank and matched patients with ankylosing spondylitis to controls without AS or axial spondyloarthritis, using routine primary care data.\"},{\"question\":\"How were the machine learning models constructed and validated?\",\"answer\":\"Models were developed with feature/variable selection and principal component analysis to build decision trees. The decision tree with the highest average F value was selected and validated using a test dataset.\"},{\"question\":\"Which predictors differed between men and women in the developed models?\",\"answer\":\"For men, lower back pain, uveitis, and NSAID use under age 20 were associated with AS development. For women, symptom presentation at older ages compared with men, alongside back pain and multiple pain relief medications, characterized the model output.\"}]","Predicting a diagnosis of ankylosing spondylitis using primary care health records - A machine learning approach | PDF",1785900234,40,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-a-diagnosis-of-ankylosing-spondylitis-using-primary-care-health-records-a-machine-learning-approach","",{"@graph":36,"@context":85},[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/predicting-a-diagnosis-of-ankylosing-spondylitis-using-primary-care-health-records-a-machine-learning-approach/125615/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data source and study design were used to predict ankylosing spondylitis diagnoses?","Question",{"text":75,"@type":76},"The study used the Secure Anonymised Information Linkage databank and matched patients with ankylosing spondylitis to controls without AS or axial spondyloarthritis, using routine primary care data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models constructed and validated?",{"text":80,"@type":76},"Models were developed with feature/variable selection and principal component analysis to build decision trees. The decision tree with the highest average F value was selected and validated using a test dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"Which predictors differed between men and women in the developed models?",{"text":84,"@type":76},"For men, lower back pain, uveitis, and NSAID use under age 20 were associated with AS development. For women, symptom presentation at older ages compared with men, alongside back pain and multiple pain relief medications, characterized the model output.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]