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 |
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| Authors: | Konstantinos Tsioufis; Despoina Ntiloudi; George Giannakoulas; George Lazaros; Constantinos Bakogiannis; George Kassimis; Anastasios Kartas; Panos E Vardas; Dimitrios Farmakis; Periklis Davlouros; Maria Ioannou; George Kochiadakis; Athanasios Samaras; Antonios Ziakas; Theoni Theodoropoulou; Dimitrios V Moysidis; John Skoularigis; Andreas S Papazoglou; Alexandra Bekiaridou; Grigorios Tsoumakas; Panagiotis Bamidis; Grigorios Tsigkas; Nikolaos Fragakis; Vassilios Vassilikos; Ioannis Zarifis; Dimitrios N Tziakas; Athanasios Feidakis; Vasiliki Patsiou; Eirinaios Tsiartas; Antonios Orfanidis; Triantafyllia Grantza; Chrysanthi Ioanna Lampropoulou; Dimitrios Kostakakis; Olga Kazarli; Maria Eirini Kiriakideli; Melina Kyriakou; Dimitra Kontopyrgou; Martha Zergioti; Eleftherios Gemousakakis; Amalia Baroutidou; Alexios Vagianos; Alexandros Liatsos; Konstantinos Barmpagiannos; George Tyrikos; George Konstantinou; Anthi Vasilopoulou; Marina Spaho; Eleni Manthou; Panagiotis Zymaris; Eleni Baliafa; Maria Baloka; Iasonas Dermitzakis; Vasiliki Anagnostopoulou; Chrysi Solovou; Anna Maria Louka; Aliki Iliadou; Ioanna Filimidou; Aspasia Kyriafini; Odysseas Kamzolas; Ioannis Vouloagkas; Despoina Nteli; Nikolaos Outountzidis; Athanasia Vathi; Anastasia Foka; Michael Botis; Anastasia Christodoulou; George Vogiatzis; Eleni Vrana; Maria Nteli; Stefanos Antοniadis; Foteini Charisi; Mairifylli Vamvaka; Dimitrios Triantis; Efi Delilampou; Vaggelis Axarloglou; Georgios Charistos; George Anagnostou; Sofia Christodoulou; Anastasios Papanastasiou; Eleni Tziona; Nikolaos Batis; Katerina Gakidi; Artemis Iosifidou; Andreanna Moura; Christos Alexandropoulos; Theoni Exintaveloni; Asterios Karakoutas; Damianos Porfyropoulos; Michail Bountas; Athanasios Pachoumis; Eleftherios Markidis; Maria Sitmalidou; Athanasia Pappa; Konstantinos C Theodoropoulos; George Rampidis; Apostolos Tzikas; Stylianos Paraskevaidis; Georgios Efthimiadis; Theofilatos Athinagoras; Christoforos Travlos; Nikolaos Vythoulkas-Biotis; Kassiani Maria Nastouli; Nikolaos Kartas; Angeliki Vakka; Maria Bozika; Virginia Anagnostopoulou; Georgios Tsioulos; Emilia Lazarou; Panagiotis Tsioufis; Ioannis Kachrimanidis; Nick Argyriou; Emmanouil Kampanieris; Alexandros Patrianakos; Ioannis Kanakakis; Marios Vasileios Koutroulos; Georgios K Chalikias; Sophia Alexiou; Athena Nasoufidou; Panagiotis Stachteas; Tsantikos Christos; Grigorios Giamouzis; Ioannis Alexanian; Ioannis Styliadis; George Fotos; Nikolaos Bourboulis; Evangelos Pisimisis; Antonis Billis; Ilias Kyparissidis; Dimitrios Tsalikakis; Jens-Michael Papaioannou; Alexander Löser |
| Source: | BMJ Open, Vol 13, Iss 4 (2023) |
| Publisher Information: | BMJ Publishing Group |
| Publication Year: | 2023 |
| Collection: | Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: | Medicine |
| 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 ... |
| Document Type: | article in journal/newspaper |
| Language: | English |
| Relation: | https://bmjopen.bmj.com/content/13/4/e068698.full; https://doaj.org/toc/2044-6055; https://doaj.org/article/7d975a30893b4c9eb09d38d1fc4cfba7 |
| DOI: | 10.1136/bmjopen-2022-068698 |
| Availability: | https://doi.org/10.1136/bmjopen-2022-068698; https://doaj.org/article/7d975a30893b4c9eb09d38d1fc4cfba7 |
| Accession Number: | edsbas.90FF8A1B |
| Database: | BASE |