Clinical Trial Signal Prediction Using Machine Learning - IQVIA Patent

ChangeBridge: Patent Grants - Health Informatics (G16H)

Published March 31st, 2026

Detected March 31st, 2026

Summary

USPTO granted patent US12592319B1 to IQVIA Inc. covering machine learning methods for predicting clinical trial efficacy and safety signals. The patent describes systems that collect clinical data from multiple sources, standardize and redact personally identifiable information, and train machine learning models to generate predictive signals for enrolled trial subjects. The 20-claim patent was applied for in March 2024.

View original document View source feed page

What changed

USPTO granted patent US12592319B1 to IQVIA Inc. on March 31, 2026. The patent covers methods and systems for detecting clinical trial signals using machine learning, including collecting clinical data from multiple sources, standardizing and redacting personally identifiable information, determining predictive features correlating with efficacy and safety signals, and training ML models to predict signals for enrolled subjects.

This patent grant does not impose compliance obligations on pharmaceutical companies or healthcare providers. Organizations conducting clinical trials may wish to review IQVIA's patent claims when developing or licensing similar signal prediction technologies to assess potential intellectual property considerations. No regulatory deadlines or penalties are associated with this issuance.

Source document (simplified)

← USPTO Patent Grants

Early prediction of clinical trial signals

Grant US12592319B1 Kind: B1 Mar 31, 2026

Assignee

IQVIA Inc.

Inventors

Gary Shorter, Naouel Baili Benabdallah, Brian Slocum Vannah

Abstract

Methods and systems including computer programs encoded on computer storage media, for a method for detecting signals related to subjects participating in a clinical trial. In some implementations, a computer collects clinical data from multiple sources. The computer standardizes and redacts personally identifiable information and determines predictive features that correspond to characteristics of the data that correlate with efficacy and safety signals. The computer obtains training data and trains machine learning models to predict one or both of a predicted efficacy signal and a predicted safety signal for a subject. The computer receives clinical data for a particular clinical trial subject enrolled in an ongoing clinical trial and predicts one or more signals and receives a review of the signals through a user interface. The computer updates the predictive components of the system based on the review.