Methods and systems for dynamic constitutional guidance using artificial intelligence

A method of dynamic conditional guidance using artificial intelligence, the method comprising:

• calculating by a computing device, a diagnostic output using a biological extraction related to a user and a first machine-learning process, wherein the diagnostic output identifies a prognostic label and an ameliorative label, wherein the prognostic label is a medical condition affecting the user, wherein the ameliorative label is configured to alleviate the medical condition affecting the user;
• receiving by the computing device, training data, wherein receiving the training data further comprises receiving a first training set including a plurality of data entries, wherein each first data entry of the plurality of first data entries comprising at least an element of physiological state data and at least a correlated first prognostic label, wherein the physiological state data comprises descriptions of the user's physiological state entered by the user;
• generating by the computing device, a machine learning model using the first machine-learning process, wherein generating the machine learning model comprises an input layer of nodes, at least one intermediate layers, and an output layer of nodes, wherein a connection between nodes is created, wherein the connections between nodes in adjacent layers are adjusted to produce desired values at the output nodes;
• outputting by the computing device, using the machine learning model, the diagnostic output;
• classifying by the computing device, using a physiological classifier and a first classification algorithm, the diagnostic output to a physiological state for the user wherein the first classification algorithm derives from the training data a physiological classifier, wherein the physiological classifier utilizes the diagnostic output as an input and outputs a physiological state, and wherein the physiological classifier is iteratively updated using classification data as a function of the training data;
• generating by the computing device, a vector output for the physiological state for the user using a clustering algorithm;
• receiving by the computing device, a user input generated in response to the diagnostic output;
• updating by the computing device, the vector output using the user input;
• and
• identifying by the computing device, a recommendation for the user, utilizing the updated vector output.