ArticleBMJ open2023
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.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05176769 (The Usefulness of Artificial Intelligence for Automated Extraction and Processing of Clinical Data From Electronic Medical Records), which is not on this map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Usefulness of Artificial Intelligence for Automated Extraction and Processing of Clinical Data From Electronic Medical Records (CardioMining-AI)
Who cites it
13 citing papers in PubMed.
- Precision medicine and personalized nursing in cardiovascular disease: clinical applications and frontier developments.Frontiers in cardiovascular medicine · 2026Review
- Knowledge, Attitude, and Practice of Artificial Intelligence (AI) among Rheumatology Professionals: An Online Cross-Sectional Survey.Mediterranean journal of rheumatology · 2025Article
- Myocardial Infarction with Obstructive, Non-Obstructive, and Mimicking Conditions: Clinical Phenotypes, Diagnostic Imaging, Management, and Prognosis.Journal of clinical medicine · 2025Article
- Considerations and Challenges When Using Clinical and Vital Record Review for Suicide Research.Journal of patient safety · 2025Article
- Facilitators and Barriers to Uptake of Drug-Drug Interaction Alerts: Perspectives of Australian End Users and Managers.Applied clinical informatics · 2025Article
- A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications.Clinical and translational science · 2025Article
- Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics.European heart journal. Digital health · 2025Article
- Review
- Integrating the Polysocial Risk Score: Enhancing Comprehensive Healthcare Delivery.Methodist DeBakey cardiovascular journal · 2024Review
- Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care.Diagnostics (Basel, Switzerland) · 2023Review
- Polysocial Risk Scores: Implications for Cardiovascular Disease Risk Assessment and Management.Current atherosclerosis reports · 2023Review
- Article
- Effectiveness of early pharmaceutical interventions in symptomatic COVID-19 patients: A randomized clinical trial.Pakistan journal of medical sciencesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMining 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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