Τίτλος:
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
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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. © 2023 BMJ Publishing Group. All rights reserved.
Συγγραφείς:
Samaras, A.
Bekiaridou, A.
Papazoglou, A.S.
Moysidis, D.V.
Tsoumakas, G.
Bamidis, P.
Tsigkas, G.
Lazaros, G.
Kassimis, G.
Fragakis, N.
Vassilikos, V.
Zarifis, I.
Tziakas, D.N.
Tsioufis, K.
Davlouros, P.
Giannakoulas, G.
Περιοδικό:
BMJ Open Gastroenterology
Εκδότης:
BMJ Publishing Group
Λέξεις-κλειδιά:
adult; Article; artificial intelligence; big data; cardiac patient; cardiovascular risk; cardiovascular system; clinical decision support system; controlled study; data mining; demographics; ecosystem; electronic health record; female; Greece; health care system; hospital discharge; human; laboratory test; major clinical study; male; medical history; multicenter study; national health organization; natural language processing; phenotype; practice guideline; retrospective study; support vector machine; tertiary care center; aftercare; artificial intelligence; ecosystem; methodology; multicenter study (topic), Aftercare; Artificial Intelligence; Cardiovascular System; Ecosystem; Electronic Health Records; Humans; Multicenter Studies as Topic; Patient Discharge; Research Design; Retrospective Studies
DOI:
10.1136/bmjopen-2022-068698
