[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126354-en":3,"doc-seo-126354-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126354,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine learning-enabled systematic review on coded healthcare data in heart failure research","Coded healthcare data are widely used in clinical research, yet reporting transparency varies across heart failure studies. This work evaluates transparency in published heart failure research and applies machine learning to support large-scale assessment. A systematic search identified 4,279 studies with accessible XML across major journals; manual review and a trained NLP model were used to quantify coding usage, dataset construction and linkage description, and to automate identification with strong internal accuracy.","University of Birmingham  \nMachine learning-enabled systematic review on coded healthcare data in heart failure research  \nChampsi, Asgher; Slater, Karin T. ; Gill, Simrat; Dyszynski, Tomasz; Schröder, Megan; Suzart-Woischnik, Kiliana; Tyl, Benoît; Allee, Guillaume ; Sartorius, Alfonso ; Lumbers, R Thomas; Asselbergs, Folkert W; Grobbee, Diederick E; Gkoutos, Georgios; Kotecha, Dipak DOI:  \n10.1093/ehjdh/ztaf123  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPeer reviewed version  \nCitation for published version (Harvard):  \nChampsi, A, Slater, KT, Gill, S, Dyszynski, T, Schröder, M, Suzart-Woischnik, K, Tyl, B, Allee, G, Sartorius, A, Lumbers, RT, Asselbergs, FW, Grobbee, DE, Gkoutos, G & Kotecha, D 2025, 'Machine learning-enabled systematic review on coded healthcare data in heart failure research', European Heart Journal. [https://doi.org/10.1093/ehjdh/ztaf123](https://doi.org/10.1093/ehjdh/ztaf123)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. 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Aug. 2026  \n1 Machine learning-enabled systematic review on coded healthcare data in  \n2 heart failure research  \n3 Asgher Champsi1,2* , Karin T. Slater3,4* , Simrat Gill1 , Tomasz Dyszynski5 , Megan Schröder6 , 4 Kiliana Suzart-Woischnik5 , Benoit Tyl7 , Guillaume Allée8 , Alfonso Sartorius9 , R. Thomas  \n5 Lumbers 10 , Folkert W Asselbergs 10,11,12 , Diederick E. Grobbee 13 , Georgios Gkoutos3,4 , Dipak  \n6 Kotecha 1,2,13  \n7 * Joint first authors.  \n8 1 Department of Cardiovascular Sciences, University of Birmingham, Birmingham, UK. 2 NIHR  \n9 Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, 10 Birmingham, UK. 3 Centre for Health Data Science, University of Birmingham, Birmingham, UK. 4  \n11 Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK. 5 Bayer AG, Berlin, 12 Germany. 6 Boehringer Ingelheim, Ingelheim, Germany. 7 Bayer Healthcare SAS, La Garenne- 13 Colombes, France. 8 Servier Laboratories, Paris, France. 9 Servier Laboratories, Madrid, Spain. 10  \n14 Institute of Health Informatics, University College London, London, UK. 11 The National Institute for  \n15 Health and Care Research (NIHR) University College London Biomedical Research Centre (BRC), 16 University College London, London, UK. 12 Department of Cardiology, Amsterdam University Medical  \n17 Center, Amsterdam, the Netherlands. 13 University Medical Center Utrecht, Utrecht University, Utrecht, 18 the Netherlands.  \n19 Word counts: Abstract: 250; Text: 2749 Figures: 4 (incl. vis","cbCaiastGALhacI5","https://ap.wps.com/l/cbCaiastGALhacI5","pdf",652266,5,1,28,"English","en",105,"# Abstract\n## Aims\n## Methods & Results\n## Keywords","[{\"question\":\"What is the main aim of the systematic review?\",\"answer\":\"To assess transparency of reporting in heart failure studies that use coded healthcare data and to use machine learning to enable larger-scale evaluation.\"},{\"question\":\"Which databases and time range were searched?\",\"answer\":\"EMBASE and MEDLINE for studies published between 2015 and 2020.\"},{\"question\":\"How was machine learning applied in the study?\",\"answer\":\"A NLP model was trained using manually annotated studies to automate and upscale the review, achieving high internal accuracy (AUC 0.97, F1 0.96) and identifying additional studies reporting coded data.\"}]","Machine learning-enabled systematic review on coded healthcare data in heart failure research | PDF",1785904634,71,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-enabled-systematic-review-on-coded-healthcare-data-in-heart-failure-research","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-enabled-systematic-review-on-coded-healthcare-data-in-heart-failure-research/126354/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main aim of the systematic review?","Question",{"text":77,"@type":78},"To assess transparency of reporting in heart failure studies that use coded healthcare data and to use machine learning to enable larger-scale evaluation.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which databases and time range were searched?",{"text":82,"@type":78},"EMBASE and MEDLINE for studies published between 2015 and 2020.",{"name":84,"@type":75,"acceptedAnswer":85},"How was machine learning applied in the study?",{"text":86,"@type":78},"A NLP model was trained using manually annotated studies to automate and upscale the review, achieving high internal accuracy (AUC 0.97, F1 0.96) and identifying additional studies reporting coded data.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"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":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]