Abstract: Techniques for generating a user interface view including sensor data representative of analyte levels of a host are disclosed. In certain embodiments, a technique includes accessing sensor data including a plurality of analyte readings of the host during a plurality of time periods, wherein each analyte reading is indicative of an analyte level of the host at a respective time. The technique further includes generating a first user interface (UI) view comprising one or more UI elements based on the plurality of analyte levels of the host, and, in response to receiving a user selection of a pregnancy mode, automatically modifying a parameter of at least one UI element included in the one or more UI elements to reflect a pregnancy-specific parameter. The technique further includes generating a second UI view based on the plurality of analyte levels of the host and the pregnancy-specific parameter.

Abstract: Disease prediction using analyte measurements and machine learning is described. In one or more implementations, a combination of features of analyte measurements may be selected from a plurality of features of the analyte measurements based on a robustness metric and a performance metric of the combination, and a machine learning model may be trained to predict a health condition classification using the combination. The performance metric may be associated with an accuracy of predicting the health condition classification, and the robustness metric may be associated with an insensitivity to analyte sensor manufacturing variabilities on the accuracy. Once trained, the machine learning model predicts the health condition classification for a user based on analyte measurements of the user collected by a wearable analyte monitoring device. The combination of features may be extracted from the analyte measurements of the user and input into the machine learning model to predict the classification.