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- W2772509702 abstract "Big data has been revolutionizing individualized medicine, improving the results of diagnostic imaging, genetic testing, and by providing frameworks for electronic health record sharing and analysis. In this paper we present a nextstep in personalized data-driven health by demonstrating the capability of predictive individualized models to model peak postprandial plasma glucose concentrations. Past research has shown that postprandial plasma glucose concentrations are highly predictive of both the development of type 2 diabetes, β-cell damage in the pancreas due to glucose toxicity, and a wide range of cardiovascular complications. We present a framework for individualized data collection, model parameterization, and prediction which allows for ongoing estimation of postprandial glucose after short training windows to aid in determining when an individual may be at risk for developing pre-diabetes, type 2 diabetes, or complications due to high peak postprandial blood glucose." @default.
- W2772509702 created "2017-12-22" @default.
- W2772509702 creator A5083079975 @default.
- W2772509702 date "2017-11-01" @default.
- W2772509702 modified "2023-09-26" @default.
- W2772509702 title "Fraise: A framework for predicting peak postprandial blood glucose using personalized data-driven modeling" @default.
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- W2772509702 doi "https://doi.org/10.1109/bibm.2017.8217809" @default.
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