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Abstract:
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Rates of common chronic diseases such as cardiovascular disease and hypertension are increasing. Predicting who is likely to be diagnosed with a common chronic disease is important for communicating risk to the public, healthcare providers, and patients. A family history of a common chronic disease is one of the strongest predictors of future disease risk. Recently, innovative studies have explored the potential use of population-based electronic healthcare records to construct histories of chronic diseases for parents and offspring. This area of research represents many new opportunities for statisticians; methods must be tested to accurately link family members, efficiently extract health history information from massive repositories of healthcare records, and construct disease risk prediction models using large numbers of family health history measures. This roundtable session will explore methodological topics relevant to family health history research, including study design and model development using machine-learning methods. Participants will discuss family health histories as a component of precision healthcare, which aims to offer tailored care to patients.
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