Cargando…

Survey and Evaluation of Hypertension Machine Learning Research

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 growin...

Descripción completa

Detalles Bibliográficos
Autores principales: du Toit, Clea, Tran, Tran Quoc Bao, Deo, Neha, Aryal, Sachin, Lip, Stefanie, Sykes, Robert, Manandhar, Ishan, Sionakidis, Aristeidis, Stevenson, Leah, Pattnaik, Harsha, Alsanosi, Safaa, Kassi, Maria, Le, Ngoc, Rostron, Maggie, Nichol, Sarah, Aman, Alisha, Nawaz, Faisal, Mehta, Dhruven, Tummala, Ramakumar, McCallum, Linsay, Reddy, Sandeep, Visweswaran, Shyam, Kashyap, Rahul, Joe, Bina, Padmanabhan, Sandosh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227215/
https://www.ncbi.nlm.nih.gov/pubmed/37119074
http://dx.doi.org/10.1161/JAHA.122.027896
_version_ 1785050720422068224
author du Toit, Clea
Tran, Tran Quoc Bao
Deo, Neha
Aryal, Sachin
Lip, Stefanie
Sykes, Robert
Manandhar, Ishan
Sionakidis, Aristeidis
Stevenson, Leah
Pattnaik, Harsha
Alsanosi, Safaa
Kassi, Maria
Le, Ngoc
Rostron, Maggie
Nichol, Sarah
Aman, Alisha
Nawaz, Faisal
Mehta, Dhruven
Tummala, Ramakumar
McCallum, Linsay
Reddy, Sandeep
Visweswaran, Shyam
Kashyap, Rahul
Joe, Bina
Padmanabhan, Sandosh
author_facet du Toit, Clea
Tran, Tran Quoc Bao
Deo, Neha
Aryal, Sachin
Lip, Stefanie
Sykes, Robert
Manandhar, Ishan
Sionakidis, Aristeidis
Stevenson, Leah
Pattnaik, Harsha
Alsanosi, Safaa
Kassi, Maria
Le, Ngoc
Rostron, Maggie
Nichol, Sarah
Aman, Alisha
Nawaz, Faisal
Mehta, Dhruven
Tummala, Ramakumar
McCallum, Linsay
Reddy, Sandeep
Visweswaran, Shyam
Kashyap, Rahul
Joe, Bina
Padmanabhan, Sandosh
author_sort du Toit, Clea
collection PubMed
description 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. 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. 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.
format Online
Article
Text
id pubmed-10227215
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-102272152023-05-31 Survey and Evaluation of Hypertension Machine Learning Research du Toit, Clea Tran, Tran Quoc Bao Deo, Neha Aryal, Sachin Lip, Stefanie Sykes, Robert Manandhar, Ishan Sionakidis, Aristeidis Stevenson, Leah Pattnaik, Harsha Alsanosi, Safaa Kassi, Maria Le, Ngoc Rostron, Maggie Nichol, Sarah Aman, Alisha Nawaz, Faisal Mehta, Dhruven Tummala, Ramakumar McCallum, Linsay Reddy, Sandeep Visweswaran, Shyam Kashyap, Rahul Joe, Bina Padmanabhan, Sandosh J Am Heart Assoc Original Research 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. 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. 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. John Wiley and Sons Inc. 2023-04-29 /pmc/articles/PMC10227215/ /pubmed/37119074 http://dx.doi.org/10.1161/JAHA.122.027896 Text en © 2023 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
du Toit, Clea
Tran, Tran Quoc Bao
Deo, Neha
Aryal, Sachin
Lip, Stefanie
Sykes, Robert
Manandhar, Ishan
Sionakidis, Aristeidis
Stevenson, Leah
Pattnaik, Harsha
Alsanosi, Safaa
Kassi, Maria
Le, Ngoc
Rostron, Maggie
Nichol, Sarah
Aman, Alisha
Nawaz, Faisal
Mehta, Dhruven
Tummala, Ramakumar
McCallum, Linsay
Reddy, Sandeep
Visweswaran, Shyam
Kashyap, Rahul
Joe, Bina
Padmanabhan, Sandosh
Survey and Evaluation of Hypertension Machine Learning Research
title Survey and Evaluation of Hypertension Machine Learning Research
title_full Survey and Evaluation of Hypertension Machine Learning Research
title_fullStr Survey and Evaluation of Hypertension Machine Learning Research
title_full_unstemmed Survey and Evaluation of Hypertension Machine Learning Research
title_short Survey and Evaluation of Hypertension Machine Learning Research
title_sort survey and evaluation of hypertension machine learning research
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227215/
https://www.ncbi.nlm.nih.gov/pubmed/37119074
http://dx.doi.org/10.1161/JAHA.122.027896
work_keys_str_mv AT dutoitclea surveyandevaluationofhypertensionmachinelearningresearch
AT trantranquocbao surveyandevaluationofhypertensionmachinelearningresearch
AT deoneha surveyandevaluationofhypertensionmachinelearningresearch
AT aryalsachin surveyandevaluationofhypertensionmachinelearningresearch
AT lipstefanie surveyandevaluationofhypertensionmachinelearningresearch
AT sykesrobert surveyandevaluationofhypertensionmachinelearningresearch
AT manandharishan surveyandevaluationofhypertensionmachinelearningresearch
AT sionakidisaristeidis surveyandevaluationofhypertensionmachinelearningresearch
AT stevensonleah surveyandevaluationofhypertensionmachinelearningresearch
AT pattnaikharsha surveyandevaluationofhypertensionmachinelearningresearch
AT alsanosisafaa surveyandevaluationofhypertensionmachinelearningresearch
AT kassimaria surveyandevaluationofhypertensionmachinelearningresearch
AT lengoc surveyandevaluationofhypertensionmachinelearningresearch
AT rostronmaggie surveyandevaluationofhypertensionmachinelearningresearch
AT nicholsarah surveyandevaluationofhypertensionmachinelearningresearch
AT amanalisha surveyandevaluationofhypertensionmachinelearningresearch
AT nawazfaisal surveyandevaluationofhypertensionmachinelearningresearch
AT mehtadhruven surveyandevaluationofhypertensionmachinelearningresearch
AT tummalaramakumar surveyandevaluationofhypertensionmachinelearningresearch
AT mccallumlinsay surveyandevaluationofhypertensionmachinelearningresearch
AT reddysandeep surveyandevaluationofhypertensionmachinelearningresearch
AT visweswaranshyam surveyandevaluationofhypertensionmachinelearningresearch
AT kashyaprahul surveyandevaluationofhypertensionmachinelearningresearch
AT joebina surveyandevaluationofhypertensionmachinelearningresearch
AT padmanabhansandosh surveyandevaluationofhypertensionmachinelearningresearch