Methods for predicting relative benefit of therapy options and devices thereof

A method for improved machine learning modeling for predicting relative benefit of therapy options for a condition, the method comprising:

• generating training cohorts and validation cohorts for each of first and second therapy options for a plurality of patients based on training and validation data for the patients obtained via one or more communication networks from one or more data server devices and comprising at least first molecular and clinical data;
• generating an ensemble machine learning model, comprising:
• sampling training and cross-validation subsets from each of the training cohorts and validation subsets from each of the validation cohorts comprising excluding from the training cohorts and the validation cohorts a set of other patients that received treatment including one or more agents aligned to both the first and second therapy options;
• training machine learning models using the training and cross-validation subsets, wherein the machine learning models comprise random survival forest (RSF) models trained to predict treatment response data for each of the first and second therapy options;
• validating the machine learning models using treatment-specific response data in the validation subsets to determine that the machine learning models exceed an accuracy threshold, wherein the treatment-specific response data comprises first progression-free survival data;
• and
• combining first and second subsets of the machine learning models into the ensemble machine learning model, wherein at least a subset of models in each of the first and second subsets of the machine learning models vary by tree depth and parameter set as a result of the training;
• applying the machine learning model to patient data for a patient to generate first and second treatment response data, each corresponding to one of a plurality of categories for each of the first and second therapy options, wherein the patient data is obtained via the communication networks from a client device;
• generating, and outputting via the communication networks for display via a display device of the client device, a graphical user interface comprising a representation of the first and second treatment response data with respect to the categories for the first and second therapy options, respectively, to thereby inform treatment of the patient for the condition;
• and
• treating the patient in accordance with one of the first or second therapy options as a frontline therapy based on a relative value of the first and second treatment response data, wherein the first and second therapy options comprise Gemcitabine/nab-Paclitaxel (GA) and FOLFIRINOX (FFX) chemotherapeutic agents and the treatment comprises administering chemotherapy to the patient via the GA or FFX chemotherapeutic agents to treat pancreatic adenocarcinoma (PDAC).