Methods and systems for informing self-treatment remedy selection

A system for informing self-treatment remedy selection, the system comprising a computing device, the computing device designed and configured to:

• identify a self-treatment remedy associated with a user, wherein identifying the self-treatment remedy associated with the user comprises:
• generating a machine-learning treatment model, wherein generating the machine-learning treatment model comprises:
• training the machine-learning treatment model using a first machine learning algorithm and treatment training data, wherein the treatment training data comprises a plurality of biological extractions comprising at least a user body measurement including an epigenetic body measurement of a user disease state describing changes to a user genome that do not involve corresponding changes in a user nucleotide sequence, and a plurality of correlated self-treatment remedies;
• receiving a first user biological extraction;
• and
• outputting, using the trained machine-learning treatment model, a first potential self-treatment remedy, wherein the first user biological extraction is provided to the trained machine-learning treatment model as an input to output the first potential self-treatment remedy;
• generating a machine-learning symptomatic model, wherein generating the machine-learning symptomatic model comprises:
• training the machine-learning symptomatic model using a second machine learning algorithm and symptomatic training data, wherein the symptomatic training data comprises a plurality of symptoms and a plurality of correlated self-treatment remedies;
• receiving a user symptom;
• and
• outputting, using the trained machine-learning symptomatic model, a second potential self-treatment remedy, wherein the first user biological extraction is provided to the trained machine-learning symptomatic model as an input to output the second potential self-treatment remedy;
• determining an identifier from a scan received from a user device associated with the user;
• and
• generating the identified self-treatment remedy as a function of the first potential self-treatment remedy, the second potential self-treatment remedy and the identifier;
• classify, using a remedy classifier, the identified self-treatment remedy;
• generate, using the remedy classifier, a self-treatment model based upon the classification of the identified self-treatment remedy, said self-treatment model comprising a third machine learning algorithm configured to receive a second user biological extraction as an input and output a remedy label, wherein generating the self-treatment model comprises training the self-treatment model according to the classification of the identified self-treatment remedy, wherein said remedy label indicates whether the identified self-treatment remedy is safe for consumption by the user;
• determine, using the remedy label, an indication of safety for user consumption of the identified self-treatment remedy, wherein determining the indication of safety comprises:
• retrieving an element of user metabolic data relating to absorption, distribution, metabolism, and elimination of self-treatment remedies;
• generate a metabolic model, wherein the metabolic model comprises a trained machine-learning model configured to receive the element of user metabolic data associated with the user as an input and output at least one safe treatment remedy based on the absorption, distribution, metabolism, and elimination of self-treatment remedies;
• and
• compare the at least one safe treatment remedy to the identified self-treatment remedy.