1st Department of Cardiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece.
Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, New York, New York, USA.
BMJ Open. 2023 Apr 3;13(4):e068698. doi: 10.1136/bmjopen-2022-068698.
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.
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.
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.
NCT05176769.
电子健康记录(EHR)数据挖掘在全球范围内越来越多地得到实施,但主要集中在结构化数据上。人工智能(AI)的功能可以扭转对非结构化 EHR 数据的使用不足,并提高医学研究和临床护理的质量。本研究旨在开发一种基于人工智能的模型,将非结构化 EHR 数据转化为有组织、可解释的数据集,并形成一个全国性的心脏病患者数据集。
CardioMining 是一项回顾性、多中心研究,基于从希腊最大的三级医院的非结构化 EHR 中获得的大型、纵向数据。将收集人口统计学、医院管理数据、病史、药物、实验室检查、影像学报告、治疗干预、住院管理和出院后医嘱,以及来自国家卫生研究院的结构化预后数据。纳入患者的目标数量为 100000 人。自然语言处理技术将有助于从非结构化 EHR 中进行数据挖掘。自动化模型的准确性将与研究调查人员的手动数据提取进行比较。机器学习工具将提供数据分析。CardioMining 旨在培养国家心血管系统的数字化转型,并利用经过验证的人工智能技术填补医疗记录和大数据分析的空白。
本研究将根据国际协调良好临床实践指南、赫尔辛基宣言、欧洲数据保护局的数据保护法规和欧洲通用数据保护条例进行。塞萨洛尼基亚里士多德大学研究伦理委员会和 AHEPA 大学医院科学和伦理委员会已批准该研究。研究结果将通过同行评议的医学期刊和国际会议传播。将尝试与其他心血管登记处进行国际合作。
NCT05176769。