[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117105-en":3,"doc-seo-117105-105":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},117105,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",7,"Healthcare","Systemic lupus in the era of machine learning medicine - review","Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With access to diverse big datasets, researchers use machine learning to detect patterns and interactions in complex data more effectively than traditional statistics. These methods create new opportunities for studying SLE, a multifactorial, highly heterogeneous and complex disease. The review outlines how machine learning is being integrated into SLE research, covering prediction models, biomarker discovery, key gaps, challenges, and opportunities, while emphasizing that external validation is still required before clinical adoption.","UC Berkeley  \nUC Berkeley Previously Published Works  \nTitle  \nSystemic lupus in the era of machine learning medicine  \nPermalink  \n[https://escholarship.org/uc/item/845218j1](https://escholarship.org/uc/item/845218j1)  \nJournal  \nLupus Science & Medicine, 11(1)  \nISSN  \n2053-8790  \nAuthors  \nZhan, Kevin  \nBuhler, Katherine A Chen, Irene Yet al.  \nPublication Date  \n2024-03-01  \nDOI  \n10.1136/lupus-2023-001140  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial License, available at [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nReview  \nSystemic lupus in the era of machine learning medicine  \nKevin Zhan,1 Katherine A Buhler,1 Irene Y Chen,2,3 Marvin J Fritzler,1 May Y Choi  1,4  \nTo cite: Zhan K, Buhler KA, Chen IY, et al. Systemic  \nlupus in the era of machine learning medicine. Lupus Science & Medicine 2024;11:e001140 . doi:10 . 1136/ lupus-2023-001140  \n► Additional supplemental material is published online only. To view, please visit the journal online ([https://doi.org/10.1136/](https://doi.org/10.1136/)[ ](https://doi.org/10.1136/)[lupus-2023-001140](lupus-2023-001140)) .  \nReceived 29 December 2023 Accepted 26 January 2024  \n© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.  \n1University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada 2Computational Precision Health, University of California Berkeley and University of California San Francisco, Berkeley, California, USA  \n3Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California, USA  \n4McCaig Institute for Bone and Joint Health, Calgary, Alberta, Canada  \nCorrespondence to  \nDr May Y Choi; may.choi@ ucalgary.ca  \nABSTRACT  \nArtificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/ or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challengesand opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.  \nINTRODUCTION  \nTremendous progress in our understanding of SLE pathogenesis, diagnosis and management has been made over the past 75 years, with most studies relying on traditional statistical techniques to evaluate and test hypotheses. While these approaches are still widely used, many researchers are turning to machine learning (ML) as a complementary method for assessing patterns that are not readily tested using traditional statistics. In the last 5 years alone, there has been an explosion of studies that have leveraged the power of ML to study SLE patient identification, risk prediction, diagnosis, disease subtype cla","cbCaimAFyS2nRl0Y","https://ap.wps.com/l/cbCaimAFyS2nRl0Y","pdf",1499110,1,14,"English","en",105,"# Abstract\n# Introduction\n# What is already known on this topic\n# What this study adds\n# How this study might affect research, practice or policy","[{\"question\":\"What is the main purpose of this review on SLE and machine learning?\",\"answer\":\"To provide an overview of machine learning techniques and discuss current gaps, challenges, and opportunities for SLE studies, including how AI/ML is being integrated into the field.\"},{\"question\":\"Which machine learning outcomes are highlighted for SLE research?\",\"answer\":\"Recent work focuses on building prediction models and identifying novel biomarkers using supervised and unsupervised methods to support understanding of pathogenesis, early diagnosis, and prognosis.\"},{\"question\":\"Why is external validation important before clinical adoption?\",\"answer\":\"Most prediction models still require external validation to confirm they are effective, reliable, and safe for use in clinical practice.\"}]","Systemic lupus in the era of machine learning medicine - 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