Dynamic and locally-faithful explanation of machine learning models for precision medicine

A non-transitory machine-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features;
• generating a plurality of locally faithful explanation models for the patient for the disease states based upon the machine learning model;
• calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models, wherein the locally faithful explanation models include a linear model y=βx, where y is a value of the machine leaning model, x is a feature vector, and β is a vector of explanation coefficients;
• calculating the average explanation of the one feature of the plurality of features over a specific time period based upon the plurality of locally faithful explanation models;
• and
• providing an importance of the one selected feature of the plurality of features over time for evaluating the plurality of disease states of the patient.

A non-transitory machine-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features;
• generating a plurality of locally faithful explanation models for the patient for the disease states based upon a machine learning model;
• calculating an explanation vector based on the plurality of features over time using the locally faithful explanation models, wherein the locally faithful explanation models include a linear model, the linear model having a value of the machine learning model that is based on a feature vector and a vector of explanation coefficients for the feature vector;
• training a prediction model to predict the explanation of the machine learning model based upon the explanation vector;
• and
• providing the explanation of the machine learning model over time for evaluating the plurality of disease states of the patient.