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https://www.selleckchem.com/products/piceatannol.html With the emergence of electronic health records, the reuse of clinical data offers new perspectives for the diagnosis and management of patients with rare diseases. However, there are many obstacles to the repurposing of clinical data. The development of decision support systems depends on the ability to recruit patients, extract and integrate the patients' data, mine and stratify these data, and integrate the decision support algorithm into patient care. This last step requires an adaptability of the electronic health records to integrate learning health system tools. In this literature review, we examine the research that provides solutions to unlock these barriers and accelerate translational research structured electronic health records and free-text search engines to find patients, data warehouses and natural language processing to extract phenotypes, machine learning algorithms to classify patients, and similarity metrics to diagnose patients. Medical informatics is experiencing an impellent request to develop decision support systems, and this requires ethical considerations for clinicians and patients to ensure appropriate use of health data. Ischaemic heart disease and stroke are the leading causes of death worldwide at 119 per 100,000 and 85 per 100,000 population. For the USA, heart disease is leading cause of death at 165 per 100,000 population. In developed countries, strokes and acute myocardial infarction in the general population have fallen from smoking reduction, lifestyle modifications and therapeutic interventions including statins. In a population-based stroke study in the UK involving primary care practices, of in-hospital strokes 90% were ischaemic, and 37% occurred within 1 week of an operation. Approximately 50% of the patients were not on a statin. In the UK, there is a national screening initiative for the prevention of atherosclerotic cardiovascular disease (ASCVD) offered to people ag

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