Abstract: Certain aspects of the present disclosure relate to methods and systems for providing decision support around glucose management for patients with diabetes. Time-varying inputs including blood glucose, meal intake information, and amount of infused insulin are processed using a machine learning model to obtain predicted glucose levels for a plurality of prediction horizons and uncertainties for the predictions. A confidence interval is generated for each prediction and the confidence intervals are compared to hypo- and hyperglycemic thresholds. If a confidence interval is entirely below or entirely above the hypo- and hyperglycemic thresholds, respectively, then a decision support output is provided.

Abstract: A method of predicting future blood glucose concentrations of an individual patient includes: identifying an individualized linear black box model of glucose-insulin by estimating a plurality of impulse response functions each accounting for an input-output relation of a plurality of individualized patient data sets, the impulse response functions being functions in a Reproducing Kernel Hilbert Space (RKHS); and applying a linear predicting technique to the selected model using the identified impulse response functions to obtain a predicted blood glucose concentration of the individual patient at a future time.

Abstract: An analyte monitoring system may include an analyte sensor and a transmitter. The analyte sensor may include an indicator element that exhibits one or more detectable properties based on an amount or concentration of an analyte in proximity to the indicator element. The analyte sensor may be configured to generate one or more raw signals indicative of one or more analyte amounts or concentrations. The transmitter may be configured to receive from the analyte sensor one or more raw signals indicative of analyte concentration. The transmitter may be configured to denoise the raw signal using a real-time filtering technique with one or more time-varying parameters. The transmitter may be configured to predict ahead of time an analyte concentration based on one or more of the received one or more raw signals using one or more prediction models.

Abstract: An analyte monitoring system may include an analyte sensor and a transmitter. The analyte sensor may include an indicator element that exhibits one or more detectable properties based on an amount or concentration of an analyte in proximity to the indicator element. The analyte sensor may be configured to generate one or more raw signals indicative of one or more analyte amounts or concentrations. The transmitter may be configured to receive from the analyte sensor one or more raw signals indicative of analyte concentration. The transmitter may be configured to denoise the raw signal using a real-time filtering technique with one or more time-varying parameters. The transmitter may be configured to predict ahead of time an analyte concentration based on one or more of the received one or more raw signals using one or more prediction models.