[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124696-en":3,"doc-seo-124696-105":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},124696,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","A machine learning analysis to inflammatory myopathies - evaluate the outcome measures in","Objective: Assess the long-term outcome in patients with idiopathic inflammatory myopathies (IIM) by using artificial intelligence to evaluate damage and activity disease indexes. IIM are rare, lifelong immune-mediated disorders affecting skeletal muscle and multiple organs. Methods: Analyze 103 patients diagnosed on 2017 EULAR/ACR criteria, integrating clinical manifestations, organ involvement, treatments, creatine kinase, muscle strength (MMT8), activity (MITAX), disability (HAQ-DI), damage (MDI), and global assessments, using supervised machine-learning models. Results: Key predictors were identified, including CART predictions for MMT8, clinical features for MITAX, and MDI/HAQ-DI for damage scores.","Autoimmunity Reviews 22 (2023) 103353  \nContents lists available at ScienceDirect  \nAutoimmunity Reviews  \njournal [homepage:](homepage: www.elsevier.com/locate/autrev)[ www.elsevier.com/locate/autrev](homepage: www.elsevier.com/locate/autrev)  \n| A machine learning analysis to inflammatory myopathies |  |  | evaluate the outcome measures in |  |\n| --- | --- | --- | --- | --- |\n| Maria Giovanna Danielia, b, *, Alberto Paladinic, Eleonora Longhid, Alessandro Tonaccie, 1, Sebastiano Gangemif, 1\u003Cbr>a SOS Immunologia delle Malattie Rare e dei Trapianti, AOU delle Marche & Dipartimento di Scienze Cliniche e Molecolari, Universit`a Politecnica delle Marche, via Tronto 10/A, 60126 Torrette di Ancona, Italy\u003Cbr>b Postgraduate School of Allergy and Clinical Immunology, Universit`a Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy\u003Cbr>c Postgraduate School of Internal Medicine, Universita` Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy d Scuola di Medicina e Chirurgia, Alma Mater Studiorum, Universit`a degli Studi di Bologna, 40126 Bologna, Italy e Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), Via G. Moruzzi 1, 56124 Pisa, Italy\u003Cbr>f Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy |  |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |  |\n| Keywords:\u003Cbr>Anti-synthetase syndrome Dermatomyositis\u003Cbr>Immune-mediated necrotizing myositis Machine learning\u003Cbr>Multi-omics Outcome Polymyositis |  | Objective: To assess the long-term outcome in patients with Idiopathic Inflammatory Myopathies (IIM), focusing on damage and activity disease indexes using artificial intelligence (AI).\u003Cbr>Background: IIM are a group of rare diseases characterized by involvement of different organs in addition to themusculoskeletal. Machine Learning analyses large amounts of information, using different algorithms, decisionmaking processes and self-learning neural networks.\u003Cbr>Methods: We evaluate the long-term outcome of 103 patients with IIM, diagnosed on 2017 EULAR/ACR criteria. We considered different parameters, including clinical manifestations and organ involvement, number and typeof treatments, serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score), disability (HAQ-DI score), disease damage (MDI score), and physician and patient global assessment (PGA). The data collected were analysed, applying, with R, supervised ML algorithms such as lasso, ridge, elastic net, classification, and regression trees (CART), random forest and support vector machines (SVM) to find the factors that best predict disease outcome.\u003Cbr>Results and conclusion: Using artificial intelligence algorithms we identified the parameters that best correlate with the disease outcome in IIM. The best result was on MMT8 at follow-up, predicted by a CART regression tree algorithm. MITAX was predicted based on clinical features such as the presence of RP-ILD and skin involvement. A good predictive capacity was also demonstrated on damage scores: MDI and HAQ-DI.\u003Cbr>In the future Machine Learning will allow us to identify the strengths or weaknesses of the composite disease activity and damage scores, to validate new criteria or to implement classification criteria. |  |  |\n\n1. Introduction  \nIdiopathic inflammatory myopathies (IIM) are a group of lifelong immune-mediated disorders characterized by inflammation of skeletal  \nmuscle with involvement of other organ systems [1]. Dermatomyositis (DM), polymyositis (PM), immune-mediated necrotizing myositis (IMNM), a-hypomyopathic dermatomyositis, juvenile dermatomyositis (JDM), and inclusion body myositis (IBM) are the subtypes identified by  \nAbbreviations: ASS, Anti-synthetase syndrome; DL, Deep Learning; DM, Dermatomyositis; HAQ-DI, Health Assessment Questionnaire- Disability Index; IMNM, immune-mediated necrotizing myositis; MDI","cbCaiet7xEWpB4xY","https://ap.wps.com/l/cbCaiet7xEWpB4xY","pdf",802608,1,9,"English","en",105,"# Introduction\n## Objectives and background\n# Methods\n## Patient cohort and outcome indexes\n## Machine-learning approach\n# Results and conclusion\n## Predictive parameters and future directions","[{\"question\":\"What is the main objective of the study on inflammatory myopathies?\",\"answer\":\"To assess long-term outcomes in idiopathic inflammatory myopathies using artificial intelligence, focusing on damage and activity disease indexes.\"},{\"question\":\"How were outcomes measured in the analysis?\",\"answer\":\"The study considered clinical manifestations and organ involvement, treatment parameters, creatine kinase levels, muscle strength (MMT8), disease activity (MITAX), disability (HAQ-DI), damage (MDI), and physician/patient global assessments.\"},{\"question\":\"Which factors best predicted disease outcomes according to the machine learning results?\",\"answer\":\"MMT8 at follow-up was best predicted by a CART regression tree; MITAX was predicted using clinical features including RP-ILD and skin involvement; damage scores included MDI and HAQ-DI with demonstrated predictive capacity.\"}]","A machine learning analysis to inflammatory myopathies - evaluate the outcome measures in | PDF",1785893964,23,{"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},"a-machine-learning-analysis-to-inflammatory-myopathies-evaluate-the-outcome-measures-in","",{"@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/a-machine-learning-analysis-to-inflammatory-myopathies-evaluate-the-outcome-measures-in/124696/",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 is the main objective of the study on inflammatory myopathies?","Question",{"text":75,"@type":76},"To assess long-term outcomes in idiopathic inflammatory myopathies using artificial intelligence, focusing on damage and activity disease indexes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were outcomes measured in the analysis?",{"text":80,"@type":76},"The study considered clinical manifestations and organ involvement, treatment parameters, creatine kinase levels, muscle strength (MMT8), disease activity (MITAX), disability (HAQ-DI), damage (MDI), and physician/patient global assessments.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors best predicted disease outcomes according to the machine learning results?",{"text":84,"@type":76},"MMT8 at follow-up was best predicted by a CART regression tree; 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