Patent ID: 11948667
Assignee: INTELLIGENCIA INC.
Field: Medical technology (Instruments)
Classification: CPC G | IPC G

Claim 14:
15. A non-volatile computer-readable medium encoded with instructions for execution on a computer system, the instructions when executed by at least one processor, cause the at least one processor to perform:
storing, in a database a plurality of sets of data of clinical trials of a plurality of drugs, wherein data for each of the clinical trials in the plurality of sets of data comprises at least:
a drug of the clinical trial;
an outcome of the clinical trial for the drug;
data regarding patients of the clinical trial used to evaluate the drug; and
at least one of regulatory data associated with the clinical trial, drug molecule characteristics of the drug, and design information of the clinical trial;

training a plurality of different types of machine learning models using training data of clinical trials of the plurality of drugs from the plurality of sets of data to generate a plurality of trained machine learning models, wherein each of the plurality of different types of machine learning models is trained to take as input a drug of interest and an indication of interest and to generate as output a predicted outcome of a new clinical trial for the drug of interest and the indication of interest;
testing the plurality of trained machine learning models, comprising executing each trained machine learning model of the plurality of different types of machine learning models on testing data from the plurality of sets of data of clinical trials of the plurality of drugs to determine a prediction of a particular trial outcome and to generate associated performance data for each trained machine learning model;
selecting, based on the performance data associated with each trained machine learning model, at least one trained machine learning model and an associated type of machine learning model, comprising comparing the performance data associated with each trained machine learning model using an area under the ROC curve (AUC) metric to select the at least one trained machine learning model as performing better than at least one other model of the trained machine learning models;
executing the at least one trained machine learning model by inputting input data to the trained machine learning model for a particular clinical trial under investigation for an associated particular drug and a particular indication of the particular clinical trial and receiving as output from execution of the at least one trained machine learning model a likelihood that the particular clinical trial associated with the particular drug and the particular indication will result in the particular drug being approved to treat the particular indication;
identifying via an interface to an entity, a measure of the likelihood;
displaying, via the interface, a set of parameters for the particular clinical trial under investigation, wherein:
the set of parameters were used to execute the at least one trained machine learning model for the particular clinical trial under investigation; and
the set of parameters relate to patients of the particular clinical trial, design information of the particular clinical trial, or both;

receiving, from a user, a modification of one or more parameters of the set of parameters from one or more input controls;
responsive to receiving the one or more modified parameters, executing the at least one trained machine learning model for the particular clinical trial of interest with the modified one or more parameters and receiving as output from the trained machine learning model a modified likelihood that the particular clinical trial associated with the particular drug will result in the particular drug being approved to treat the particular indication; and
identifying, to the entity, a measure of the modified likelihood.