[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127944-en":3,"doc-seo-127944-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127944,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",7,"Healthcare","Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records","Early detection of inflammatory arthritis (IA) is essential for accurate hospital referral triage, enabling timely treatment and reducing deterioration of disease progression, particularly when healthcare resources are limited. Manual assessment is labor-intensive because clinicians must review extensive information for each GP-to-hospital referral. Machine learning can automate assessment and support decision-making, yet many approaches depend on blood test results that are often unavailable at referral time. This study proposes an ensemble multimodal method leveraging semi-structured and unstructured patient records to improve early detection performance, achieving strong metrics including precision 0.89 and accuracy 0.85.","Proceedings of the 57th Hawaii International Conference on System Sciences | 2024  \nEarly detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and  \nunstructured patient records  \nBing Wang  \nInformatics Research Centre, Henley Business School, University of Reading, UK  \n[bing.wang@pgr.reading.ac.uk](bing.wang@pgr.reading.ac.uk)  \nWeizi Li  \nInformatics Research Centre, Henley Business School, University of Reading, UK  \n[weizi.li@henley.ac.uk](weizi.li@henley.ac.uk)  \nAnthony Bradlow Rheumatology Department, Royal Berkshire NHS Foundation Trust, UK  \n[anthony.bradlow@royalberkshire.nhs.uk](anthony.bradlow@royalberkshire.nhs.uk)  \nAntoni T.Y. Chan Rheumatology Department, Royal Berkshire NHS Foundation Trust, UK  \n[antoni.chan@royalberkshire.nhs.uk](antoni.chan@royalberkshire.nhs.uk)  \nEghosa Bazuaye Informatics Department, Royal Berkshire NHS Foundation Trust, UK  \n[eghosa.bazuaye@royalberkshire.nhs.uk](eghosa.bazuaye@royalberkshire.nhs.uk)  \nAbstract  \nEarly detection of inflammatory arthritis (IA) is critical to efficient and accurate hospital referral triage for timely treatment and preventing the deterioration of the IA disease course, especially under limited healthcare resources. The manual assessment process is the most common approach in practice for the early detection of IA, but it is extremely labor-intensive and inefficient. A large amount of clinical information needs to be assessed for every referral from General Practice (GP) to the hospitals. Machine learning shows great potential in automating repetitive assessment tasks and providing decision support for the early detection of IA. However, most machine learning-based methods for IA detection rely on blood testing results. But in practice, blood testing data is not always available atthe point of referrals, so we need methods to leverage multimodal data such as semi-structured and unstructured data for early detection of IA. In this research, we present an ensemble learning-based method using multimodal data to assist decisionmaking in the early detection of IA. Experimental results show the precision, recall, F1-Score, accuracy, and G-Mean of 0.89, 0.85, 0.86, 0.85, and 0.88. To the best of our knowledge, our study is the first attempt to utilize multimodal data to support the early detection of IA from GP referrals.  \nKeywords: Early detection of inflammatory arthritis; Ensemble learning; Multimodal data.  \n1. Introduction  \nInflammatory arthritis (IA) and non-inflammatory conditions (NIC) are the two subdivisions of rheumatic musculoskeletal diseases (RMDs) . RMDs are a group of conditions that affect the bones, joints, muscles and spine, and can cause severe long-term pain and physical disability (Van Der Heijde et al., 2018) . RMDs constitute a major health problem in the general adult population due to their high prevalence and their association with significant disability, days lost at work and mortality (Government, 2022) . RMDs are a common cause of long-term disability with over 20 million people in the UK (around a third of the population) living with an MSK condition, such as arthritis and low back pain (VA, 2021) and represent 46-54% of all persons with activity limitation. Approximately 1.71 billion people have RMDs conditions worldwide (WHO, 2021) . In the UK, it has been estimated that rheumatoid arthritis alone costs the National Health Service (NHS) around £560 million per year and that additional costs to the economy of sick leave and work-related disability total £1.8 billion per year (Office, 2009) .  \nEarly detection, effective treatment and management of RMDs can improve the likelihood that they live in good health, and remain independent and connected to their community. To achieve this early detection and differentiation of inflammatory arthritis and non-inflammatory conditions is an essential step for the efficient and accurate referral in rheumatology from th","cbCaiazzNY7TXwAf","https://ap.wps.com/l/cbCaiazzNY7TXwAf","pdf",533821,2,1,9,"English","en",105,"# Abstract\n# 1. Introduction\n## Rheumatic musculoskeletal diseases and referral triage\n## Limitations of manual assessment and current ML methods","[{\"question\":\"Why is early detection of inflammatory arthritis important for referral triage?\",\"answer\":\"Early detection supports efficient and accurate triage for timely treatment and helps prevent deterioration in the IA disease course.\"},{\"question\":\"What makes existing machine learning approaches difficult to deploy at the referral stage?\",\"answer\":\"Many methods rely on blood testing results that are not always available at the time referrals are made, and referral data can vary in content and structure.\"},{\"question\":\"How does the proposed approach use multimodal data for IA early detection?\",\"answer\":\"The study presents an ensemble learning method that leverages multimodal information, including semi-structured and unstructured patient records, to assist decision-making for early detection.\"}]","Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records | PDF",1785943157,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"early-detection-of-inflammatory-arthritis-to-improve-referrals-using-multimodal-machine-learning-from-blood-testing-semi-structured-and-unstructured-patient-records","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/early-detection-of-inflammatory-arthritis-to-improve-referrals-using-multimodal-machine-learning-from-blood-testing-semi-structured-and-unstructured-patient-records/127944/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early detection of inflammatory arthritis important for referral triage?","Question",{"text":76,"@type":77},"Early detection supports efficient and accurate triage for timely treatment and helps prevent deterioration in the IA disease course.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What makes existing machine learning approaches difficult to deploy at the referral stage?",{"text":81,"@type":77},"Many methods rely on blood testing results that are not always available at the time referrals are made, and referral data can vary in content and structure.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed approach use multimodal data for IA early detection?",{"text":85,"@type":77},"The study presents an ensemble learning method that leverages multimodal information, including semi-structured and unstructured patient records, to assist decision-making for early detection.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]