System and method for clinical trial analysis and predictions using machine learning and edge computing

A system for clinical trial communications, analysis, and predictions comprising:

• a software application running on a plurality of edge computing devices, the software application running on each edge computing device being configured to:
• receive a machine learning model from a computer server, the machine learning model having been trained to predict an adverse effect of a clinical trial according to clinical trial parameters, the clinical trial parameters comprising a disease and a drug treatment for the disease;
• receive patient data from the edge device for a trial patient having the disease, the patient data comprising one or more biomarkers;
• process the patient data for the trial patient through the machine learning algorithm to obtain a predicted adverse effect on the trial patient from the drug treatment based on the patient data;
• receive an actual outcome from the edge device of drug treatment on the trial patient;
• calculate an association score by comparing the predicted adverse effect with the actual outcome;
• and
• send the patient data and the association score to the computer server;
• a computer server comprising a memory and a processor;
• a clinical trials module, comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, causes the computer server to:
• receive the clinical trial parameters;
• train the machine learning model to predict an adverse effect of a clinical trial according to the clinical trial parameters;
• deploy the machine learning model to the software application;
• receive the patient data and the association score from the software application for each of the plurality of edge computing devices;
• use the patient data and the association score from the plurality of edge computing devices to retrain the primary machine learning model;
• deploy the re-trained machine learning model to the software application;
• process the patient data from each of the plurality of edge computing devices through the re-trained machine learning algorithm to predict whether the predicted adverse effect will occur in any trial patient for which patient data has been received;
• issue an alert to the software application if the predicted adverse effect is predicted in at least one of the trial patients, the alert comprising identifying information of all the patients at risk of the predicted adverse effect.