[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121675-en":3,"doc-seo-121675-105":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},121675,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Survey and Evaluation of Hypertension Machine Learning Research - Reporting quality, validation, and algorithmic bias assessment","Hypertension ML studies are expanding rapidly, yet their translation into clinical practice remains limited, with concerns over robustness, generalizability, and the quality of reported methods and results. This research surveys 63 hypertension-related machine learning articles published from January 2019 to September 2021. It evaluates research topics, reporting quality, performance and calibration reporting, validation dataset distinctness, ethics and privacy compliance, and whether algorithmic bias and risk of bias are addressed. Results show exploratory focus and notable reporting, validation, and bias-related shortcomings.","Downloaded from [http://ahajournals.org by on August 25](http://ahajournals.org by on August 25), 2023  \nJournal of the American Heart Association  \nORIGINAL RESEARCH  \n\n| Survey and Evaluation of Hypertension Machine Learning Research\u003Cbr>Clea du Toit  , MSc*; Tran Quoc Bao Tran  , MSc*; Neha Deo  , BS; Sachin Aryal  , MS; Stefanie Lip  , MBChB; Robert Sykes  , MBChB; Ishan Manandhar  , MS; Aristeidis Sionakidis  , MSc; Leah Stevenson, MS;\u003Cbr>Harsha Pattnaik  , MBBS; Safaa Alsanosi  , PhD; Maria Kassi, MSc; Ngoc Le, MSc; Maggie Rostron  , MBChB; Sarah Nichol  , MBChB; Alisha Aman  , MSc; Faisal Nawaz  , MBBS; Dhruven Mehta  , MD;\u003Cbr>Ramakumar Tummala  , PhD; Linsay McCallum  , PhD; Sandeep Reddy  , MBBS, PhD;\u003Cbr>Shyam Visweswaran  , MD, PhD; Rahul Kashyap  , MD, MBA; Bina Joe  , PhD; Sandosh Padmanabhan  , MD, PhD\u003Cbr>BACKGROUND: Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML’s potential to transform hypertension care.\u003Cbr>METHODS AND RESULTS: The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%) . The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias.\u003Cbr>CONCLUSIONS: Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.\u003Cbr>Key Words: artificial intelligence ■ hypertension ■ machine learning ■ reporting quality |  |\n| --- | --- |\n| ecent advances in computational power and the\u003Cbr>Raivacal iladtilysofetrgerhavealnd med tooreanciomprencreaseinnsive memachie\u003Cbr>learning (ML) in clinical research, which could transform health care. Despite the rapid increase in research and evidence that ML models outperform clinicians in areas such as arrhythmia detection and clinical image | processing, the actual impact on health care has been limited.1-3 Hypertension is the single most important modifiable risk factor worldwide, causing nearly 10million deaths annually in both high-and low-income countries. The management of hypertension, from screening to diagnosis to treatment, presents a number of obstacles that call for transformational solutions in which ML may play a |\n\nCorrespondence to: Sandosh Padmanabhan, MD, PhD, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, UK. Email: [sandosh.padmanabhan@glasgow.ac.uk and Bina Joe](sandosh.padmanabhan@glasgow.ac.uk and Bina Joe), PhD, Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH 43614. Email: [bina.joe@utoledo.edu](bina.joe@utoledo.edu)  \n*C. du Toit and T. Q. B. Tran contri","cbCaiaFxjoTcikSv","https://ap.wps.com/l/cbCaiaFxjoTcikSv","pdf",1018844,1,21,"English","en",105,"# Background\n# Methods and Results\n## Research topics\n## Reporting quality and performance reporting\n## Ethics, privacy, and data security\n## Validation and bias assessment\n# Conclusions\n# Clinical Perspective","[{\"question\":\"What was the purpose of this hypertension machine learning survey and evaluation?\",\"answer\":\"To survey hypertension research using machine learning, evaluate reporting quality, and identify barriers to ML transforming hypertension care.\"},{\"question\":\"How many articles were analyzed and what time range did they cover?\",\"answer\":\"The analysis used 63 hypertension-related ML research articles published between January 2019 and September 2021.\"},{\"question\":\"What key gaps were found in reporting quality and validation practices?\",\"answer\":\"Reporting quality varied; only 46% described the study population/derivation cohort, 40% included calibration measures, and only 14% used geographically or temporally distinct validation datasets. Algorithmic bias was not addressed in any article, and few acknowledged risk of bias.\"}]","Survey and Evaluation of Hypertension Machine Learning Research - Reporting quality, validation, and algorithmic bias assessment | PDF",1785806146,53,{"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},"survey-and-evaluation-of-hypertension-machine-learning-research-reporting-quality-validation-and-algorithmic-bias-assessment","",{"@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/survey-and-evaluation-of-hypertension-machine-learning-research-reporting-quality-validation-and-algorithmic-bias-assessment/121675/",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","2026-08-04",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 was the purpose of this hypertension machine learning survey and evaluation?","Question",{"text":75,"@type":76},"To survey hypertension research using machine learning, evaluate reporting quality, and identify barriers to ML transforming hypertension care.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many articles were analyzed and what time range did they cover?",{"text":80,"@type":76},"The analysis used 63 hypertension-related ML research articles published between January 2019 and September 2021.",{"name":82,"@type":73,"acceptedAnswer":83},"What key gaps were found in reporting quality and validation practices?",{"text":84,"@type":76},"Reporting quality varied; only 46% described the study population/derivation cohort, 40% included calibration measures, and only 14% used geographically or temporally distinct validation datasets. 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