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Big biomedical data and cardiovascular disease research: opportunities and challenges
Author(s) -
Spiros Denaxas,
Katherine I. Morley
Publication year - 2015
Publication title -
european heart journal - quality of care and clinical outcomes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.766
H-Index - 18
eISSN - 2058-5225
pISSN - 2058-1742
DOI - 10.1093/ehjqcco/qcv005
Subject(s) - big data , observational study , data science , medical research , medical diagnosis , translational research , disease , raw data , health records , medicine , scale (ratio) , medical record , electronic health record , patient recruitment , computer science , health care , clinical trial , data mining , pathology , physics , quantum mechanics , economics , programming language , economic growth
Electronic health records (EHRs), data generated and collected during normal clinical care, are increasingly being linked and used for translational cardiovascular disease research. Electronic health record data can be structured (e.g. coded diagnoses) or unstructured (e.g. clinical notes) and increasingly encapsulate medical imaging, genomic and patient-generated information. Large-scale EHR linkages enable researchers to conduct high-resolution observational and interventional clinical research at an unprecedented scale. A significant amount of preparatory work and research, however, is required to identify, obtain, and transform raw EHR data into research-ready variables that can be statistically analysed. This study critically reviews the opportunities and challenges that EHR data present in the field of cardiovascular disease clinical research and provides a series of recommendations for advancing and facilitating EHR research.

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