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Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study

INTRODUCTION: Mining of electronic health record (EHRs) data is increasingly being implemented all over the world but mainly focuses on structured data. The capabilities of artificial intelligence (AI) could reverse the underusage of unstructured EHR data and enhance the quality of medical research...

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Autores principales: Samaras, Athanasios, Bekiaridou, Alexandra, Papazoglou, Andreas S, Moysidis, Dimitrios V, Tsoumakas, Grigorios, Bamidis, Panagiotis, Tsigkas, Grigorios, Lazaros, George, Kassimis, George, Fragakis, Nikolaos, Vassilikos, Vassilios, Zarifis, Ioannis, Tziakas, Dimitrios N, Tsioufis, Konstantinos, Davlouros, Periklis, Giannakoulas, George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10083759/
https://www.ncbi.nlm.nih.gov/pubmed/37012018
http://dx.doi.org/10.1136/bmjopen-2022-068698
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author Samaras, Athanasios
Bekiaridou, Alexandra
Papazoglou, Andreas S
Moysidis, Dimitrios V
Tsoumakas, Grigorios
Bamidis, Panagiotis
Tsigkas, Grigorios
Lazaros, George
Kassimis, George
Fragakis, Nikolaos
Vassilikos, Vassilios
Zarifis, Ioannis
Tziakas, Dimitrios N
Tsioufis, Konstantinos
Davlouros, Periklis
Giannakoulas, George
author_facet Samaras, Athanasios
Bekiaridou, Alexandra
Papazoglou, Andreas S
Moysidis, Dimitrios V
Tsoumakas, Grigorios
Bamidis, Panagiotis
Tsigkas, Grigorios
Lazaros, George
Kassimis, George
Fragakis, Nikolaos
Vassilikos, Vassilios
Zarifis, Ioannis
Tziakas, Dimitrios N
Tsioufis, Konstantinos
Davlouros, Periklis
Giannakoulas, George
author_sort Samaras, Athanasios
collection PubMed
description INTRODUCTION: Mining of electronic health record (EHRs) data is increasingly being implemented all over the world but mainly focuses on structured data. The capabilities of artificial intelligence (AI) could reverse the underusage of unstructured EHR data and enhance the quality of medical research and clinical care. This study aims to develop an AI-based model to transform unstructured EHR data into an organised, interpretable dataset and form a national dataset of cardiac patients. METHODS AND ANALYSIS: CardioMining is a retrospective, multicentre study based on large, longitudinal data obtained from unstructured EHRs of the largest tertiary hospitals in Greece. Demographics, hospital administrative data, medical history, medications, laboratory examinations, imaging reports, therapeutic interventions, in-hospital management and postdischarge instructions will be collected, coupled with structured prognostic data from the National Institute of Health. The target number of included patients is 100 000. Natural language processing techniques will facilitate data mining from the unstructured EHRs. The accuracy of the automated model will be compared with the manual data extraction by study investigators. Machine learning tools will provide data analytics. CardioMining aims to cultivate the digital transformation of the national cardiovascular system and fill the gap in medical recording and big data analysis using validated AI techniques. ETHICS AND DISSEMINATION: This study will be conducted in keeping with the International Conference on Harmonisation Good Clinical Practice guidelines, the Declaration of Helsinki, the Data Protection Code of the European Data Protection Authority and the European General Data Protection Regulation. The Research Ethics Committee of the Aristotle University of Thessaloniki and Scientific and Ethics Council of the AHEPA University Hospital have approved this study. Study findings will be disseminated through peer-reviewed medical journals and international conferences. International collaborations with other cardiovascular registries will be attempted. TRIAL REGISTRATION NUMBER: NCT05176769.
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spelling pubmed-100837592023-04-11 Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study Samaras, Athanasios Bekiaridou, Alexandra Papazoglou, Andreas S Moysidis, Dimitrios V Tsoumakas, Grigorios Bamidis, Panagiotis Tsigkas, Grigorios Lazaros, George Kassimis, George Fragakis, Nikolaos Vassilikos, Vassilios Zarifis, Ioannis Tziakas, Dimitrios N Tsioufis, Konstantinos Davlouros, Periklis Giannakoulas, George BMJ Open Cardiovascular Medicine INTRODUCTION: Mining of electronic health record (EHRs) data is increasingly being implemented all over the world but mainly focuses on structured data. The capabilities of artificial intelligence (AI) could reverse the underusage of unstructured EHR data and enhance the quality of medical research and clinical care. This study aims to develop an AI-based model to transform unstructured EHR data into an organised, interpretable dataset and form a national dataset of cardiac patients. METHODS AND ANALYSIS: CardioMining is a retrospective, multicentre study based on large, longitudinal data obtained from unstructured EHRs of the largest tertiary hospitals in Greece. Demographics, hospital administrative data, medical history, medications, laboratory examinations, imaging reports, therapeutic interventions, in-hospital management and postdischarge instructions will be collected, coupled with structured prognostic data from the National Institute of Health. The target number of included patients is 100 000. Natural language processing techniques will facilitate data mining from the unstructured EHRs. The accuracy of the automated model will be compared with the manual data extraction by study investigators. Machine learning tools will provide data analytics. CardioMining aims to cultivate the digital transformation of the national cardiovascular system and fill the gap in medical recording and big data analysis using validated AI techniques. ETHICS AND DISSEMINATION: This study will be conducted in keeping with the International Conference on Harmonisation Good Clinical Practice guidelines, the Declaration of Helsinki, the Data Protection Code of the European Data Protection Authority and the European General Data Protection Regulation. The Research Ethics Committee of the Aristotle University of Thessaloniki and Scientific and Ethics Council of the AHEPA University Hospital have approved this study. Study findings will be disseminated through peer-reviewed medical journals and international conferences. International collaborations with other cardiovascular registries will be attempted. TRIAL REGISTRATION NUMBER: NCT05176769. BMJ Publishing Group 2023-04-03 /pmc/articles/PMC10083759/ /pubmed/37012018 http://dx.doi.org/10.1136/bmjopen-2022-068698 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Cardiovascular Medicine
Samaras, Athanasios
Bekiaridou, Alexandra
Papazoglou, Andreas S
Moysidis, Dimitrios V
Tsoumakas, Grigorios
Bamidis, Panagiotis
Tsigkas, Grigorios
Lazaros, George
Kassimis, George
Fragakis, Nikolaos
Vassilikos, Vassilios
Zarifis, Ioannis
Tziakas, Dimitrios N
Tsioufis, Konstantinos
Davlouros, Periklis
Giannakoulas, George
Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study
title Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study
title_full Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study
title_fullStr Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study
title_full_unstemmed Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study
title_short Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study
title_sort artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the cardiomining study
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10083759/
https://www.ncbi.nlm.nih.gov/pubmed/37012018
http://dx.doi.org/10.1136/bmjopen-2022-068698
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