| CPC G16H 20/00 (2018.01) [G16H 15/00 (2018.01)] | 19 Claims |
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1. A method comprising:
receiving measurement data from a plurality of data sources, the measurement data comprising a plurality of measurements of biological parameters of the user before and after a sleep intervention;
receiving treatment data comprising one or more treatment parameters associated with the sleep intervention;
generating, using one or more processors, a plurality of quality assessment metrics based on the received measurement data, the plurality of quality assessment metrics being generated based, at least in part, on a comparison of the plurality of measurements of biological parameters before and after the sleep intervention and a comparison of a plurality of biomarkers identifying markers in a sleep profile of the user;
training a machine learning estimation model based, at least in part, on the plurality of quality assessment metrics and the received treatment data, the estimation model being configured to generate one or more predicted results associated with one or more estimation variables associated with one or more intervention treatments, the one or more estimation variables including one or more of the plurality of quality assessment metrics;
generating an inverse model, the inverse model configured to estimate one or more treatment parameters to achieve a target value of a selected quality assessment metric of the plurality of assessment metrics; and
providing the one or more treatment parameters to the machine-learning estimation model to provide an estimation of a result of another round of the sleep intervention.
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