Selecting Clinical Trial Sites Based on Multiple Target Variables Using Machine Learning

• Main Authors: Talamas, Francisco Xavier, Kip, Geoffrey Jerome, Verstraete, Hans Roeland Geert Wim, Hood, Kaitlin Ann
• Format: Patent
• Language: English
• Published: 29.08.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

Disclosed herein are methods for generating an automated method for determining or selecting one or more clinical trial sites for inclusion in a clinical trial. The method includes generating a predicted site enrollment (e.g., number of patients a site will enroll) and a predicted site default likelihood (e.g., how likely a site is to enroll zero patients or fewer patients than a predetermined threshold) for clinical trial sites by applying one or more machine learning models. The method further includes ranking the one or more clinical trial sites according to the predicted site enrollment and the predicted site default likelihood for the one or more clinical trial sites; and selecting top-ranked clinical trial sites.