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17.1 Big data in heart failure
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Published:November 2023
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Abstract
Big data describes large volume of data, both structured and unstructured, that can be mined for information using machine learning approaches. Such data are recorded routinely as part of medical care and frequently form an essential part of care delivery. These data can be acquired from healthcare systems in which the treatment, risk stratification or identification of patients with heart failure (HF), and includes electronic healthcare records, imaging data, and/or information derived from wearable devices or apps. Data derived from real world patients with HF provides important information around trajectories, clinical outcomes and response to treatment of patient groups often excluded from randomized trials, and often form the basis of national audits and public benchmarking. They help inform the impact of national policies on HF patient outcomes and should be considered as complementary to data derived from randomized controlled trials. Machine learning approaches have been leveraged in such disparate data sources to allow for the identification of novel HF phenogroups, identify patients that will respond/do not respond to treatment and have contributed to the development of prognosis models that will promote a personalized approach to patients with HF.
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