Abstract: Some embodiments of the present application include obtaining a trained machine learning model that was trained using training data. Production data may be obtained from a data feed selected based on the trained machine learning model. In some embodiments, feature sets and observed results may be extracted from data output from the data feed, and one or more features or results may be masked to generate the production data. Predicted results data may be generated with the trained machine learning model based on the production data and an accuracy score for the trained machine learning model may be determined based on the predicted results data. If the accuracy score satisfies a threshold accuracy condition, the trained machine learning model may be caused to be rebuilt or the training data may be caused to be updated.

Abstract: The invention relates to processes for making pharmaceutical aqueous therapeutic particles (AQTP) having stable exterior water clustering with nanosized thickness less than 300 nanometers, wherein the AQTP has an improved bioavailability when administered to a mammal compared to conventional pharmaceutical drug particles administered to the mammal. The invention relates to an improved process apparatus which is computer controlled, capable of continuous operation with high efficiency so as to make a more consistently acceptable AQTP compared to an previous prototype process apparatus of the Inventors.