Methods and systems for prioritizing user symptom complaint inputs

A method for prioritizing user symptom complaint inputs, the method comprises:

• receiving, by a computing device, a plurality of symptom complaint datums generated by a user;
• determining, by the computing device, a frequency element as a function of the plurality of symptom complaint datums;
• generating, by the computing device, an optimal vector output using a K-nearest neighbor (KNN) module, wherein the KNN module is configured to:
• receive suspected disease state training data correlating symptom complaint data to suspected disease state vectors;
• determine similarity between the plurality of symptom complaint datums and suspected disease states using a distance metric;
• modify suspected disease state training data by normalizing vector entries to contain consistent variance;
• generate a suspected disease state output by selecting a k-nearest neighbors and aggregating results using component-wise addition followed by normalization;
• displaying, using a graphical user interface, the generated optimal vector output;
• receiving, by the computing device, a user entry generated as a function of the displayed optimal vector output, wherein the user entry comprises a datum of user genetic history, wherein the user genetic history comprises a sample extracted from the user;
• update, by the computing device, as a function of the user entry, the generated optimal vector output;
• producing, by the computing device, a disease criticality score as a function of the frequency element and a criticality machine-learning model, wherein producing further comprises:
• iteratively training the criticality machine-learning model as a function of a criticality training set comprising exemplary frequency element inputs, and exemplary expert inputs correlated to exemplary disease criticality score outputs, wherein the updated optimal vector output is used to refine a suspected disease state ranking, wherein the suspected disease state refined ranking is utilized in selecting and updating the criticality training set;
• obtaining an expert input;
• and
• determining the disease criticality score as a function of the expert input and the frequency element using the trained criticality machine learning model;
• receiving a user-entered valuation of the disease criticality score;
• and
• updating the criticality training set as a function of the user-entered valuation;
• generating, by the computing device, a suspected disease state as a function of the disease criticality score, wherein the suspected disease state comprises a probable current disease state and at least one future disease state associated with the probable current disease state;
• and
• display the suspected disease state using the graphical user interface.