Machine-learning based efficacy predictions based on genetic and biometric information

A tangible, non-transitory computer-readable medium storing program instructions that, when executed by one or more processors, cause performance of a set of operations comprising:

• prior to receiving a request to provide an efficacy prediction for a particular cannabinoid formulation to treat a particular health condition of a user, training a machine learning model, wherein training the machine learning model comprises:
• identifying the particular health condition shared by a plurality of individuals;
• receiving respective efficacy information indicating efficacy of the particular cannabinoid formulation for treating the particular health condition shared by the plurality of individuals;
• and
• receiving respective genetic information for the plurality of individuals, wherein dimensionality of the respective genetic information has been reduced prior to training the machine learning model;
• and
• in response to receiving a request to provide the efficacy prediction for the particular cannabinoid formulation to treat the particular health condition of the user, applying the machine learning model to:
• (i) group the plurality of individuals into a plurality of groups based on their respective efficacy information and similarities in their respective genetic information;
• and
• (ii) output the efficacy prediction for the particular cannabinoid formulation to treat the particular health condition of the user, wherein the efficacy prediction is based on the trained machine learning model and the genetic information for the user;
• and
• receiving an updated indication of whether the particular cannabinoid formulation was effective in treating the particular health condition for the user;
• and
• retraining the machine learning model based on the updated indication.

A computer-implemented comprising:

• prior to receiving a request to provide an efficacy prediction for a particular cannabinoid formulation to treat a particular health condition of a user, training a machine learning model, wherein training the machine learning model comprises:
• identifying the particular health condition shared by a plurality of individuals;
• receiving respective efficacy information indicating efficacy of the particular cannabinoid formulation for treating the particular health condition shared by the plurality of individuals;
• and
• receiving respective genetic information for the plurality of individuals, wherein dimensionality of the respective genetic information has been reduced prior to training the machine learning model;
• and
• in response to receiving a request to provide the efficacy prediction for the particular cannabinoid formulation to treat the particular health condition of the user, applying the machine learning model to:
• (i) group the plurality of individuals into a plurality of groups based on their respective efficacy information and similarities in their respective genetic information;
• and
• (ii) output the efficacy prediction for the particular cannabinoid formulation to treat the particular health condition of the user, wherein the efficacy prediction is based on the trained machine learning model and the genetic information for the user;
• and
• receiving an updated indication of whether the particular cannabinoid formulation was effective in treating the particular health condition for the user;
• and
• retraining the machine learning model based on the updated indication.