Abstract: Methods to compute glycemia tests and applications thereof are described. Additional methods to compute risk of atherosclerotic cardiovascular disease and applications thereof are described. Generally, systems utilize analyte measurements to determine a glycemic status or cardiovascular disease risk, which can be used as a basis to treat individuals.

Abstract: Methods to compute glycemia tests and applications thereof are described. Additional methods to compute risk of atherosclerotic cardiovascular disease and applications thereof are described. Generally, systems utilize analyte measurements to determine a glycemic status or cardiovascular disease risk, which can be used as a basis to treat individuals.

Abstract: Methods for identifying to which patients a drug should be administered, based on underlying drug mechanism of action, are provided. Machine learning techniques are used to determine that patient's underlying disease pathway includes drug mechanism of action target; the drug is then administered, based on this determination. Multiple types of data, including demographic, physiological, treatment, and clinical notes data, can be used to train a classification component. Multiple patient populations can be used as sources of patient data for training classification component. Data input requirements, dimensionality, and performance metrics may be optimized.

Abstract: Methods for identifying to which patients a drug should be administered, based on underlying drug mechanism of action, are provided. Multiple types of data, including demographic, physiological, treatment, and clinical notes data, can be used to train a classification component. Multiple patient populations can be used as sources of patient data for training classification component. Data input requirements, dimensionality, and performance metrics may be optimized.

Abstract: Methods for determining the time at which a drug should be administered, based on patient electronic health record data. Machine learning techniques are used to correlate trends in health record data with successful drug treatment, ultimately anticipating the optimal time of drug administration. Multiple types of data, including demographic, physiological, treatment, and clinical notes data, can be used to train the classification component. Multiple patient populations can be used as sources of patient data for training classification component. Data input requirements, dimensionality, and performance metrics may be optimized.