Abstract
A call for biological data mining approaches in epidemiology.
Highlights
Epidemiology could benefit from entering the “big data” arena and has begun to do so with studies at the biologic level
U.S Census and U.S Consumer Spending data could be combined with existing clinical biorepositories and linked through a geocode to test hypotheses related to the interaction of the macro-environment and biology in disease etiology and prognosis
Epidemiology relies heavily on reductionist approaches that use standard regression models based on statistical assumptions that may not reflect the true nature of how a risk factor or group of risk factors influence disease etiology and prognosis
Summary
Epidemiology could benefit from entering the “big data” arena and has begun to do so with studies at the biologic level. U.S Census and U.S Consumer Spending data could be combined with existing clinical biorepositories and linked through a geocode to test hypotheses related to the interaction of the macro-environment and biology in disease etiology and prognosis. Analyzing big data requires knowledge and execution of data mining techniques.
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