Merative US L.p.

Salesforce, Inc.

Stephen Carraway

Kimberly Diane Kenna

Adrian Vrouwenvelder

John Hefferman

Michael John Burr

Matthew Buland

A method, computing platform, and computer program product are provided for evaluating data quality during a clinical trial. A computing platform receives, for a clinical trial, study design information including a set of parameters and corresponding parameter values related to data quality of the clinical trial. During the clinical trial, the computing platform receives query-related information associated with queries from at least some of a plurality of participants of the clinical trial. The computing platform applies the study design information and the query-related information to at least one trained machine learning model to calculate a predicted data quality score indicating data quality for the clinical trial. At least one suggestion for improving the data quality is determined and the predicted data quality score and the at least one suggestion for improving the data quality are output.

Using machine learning to evaluate data quality during a clinical trial based on participant queries

A method, computing platform, and computer program product are provided. A computing platform receives, for a clinical trial, study design information including a set of parameters related to a success of a clinical trial and factors related to a relevance of the parameters. The computing platform provides multiple sets of varying parameter values. At least one trained machine learning model is applied to the study design information including the sets of parameter values to predict multiple success scores including a predicted overall success score. The computing platform outputs the provided set of values for the set of parameters that are associated with the at least one trained machine learning model predicting a best overall success score for the clinical trial.

Using machine learning to facilitate design and implementation of a clinical trial with a high likelihood of success

A method, computing platform, and computer program product are provided for monitoring a clinical trial. A computer platform receives, for the clinical trial, study design information including a set of parameters and corresponding parameter values related to travel constraints of a subject for the clinical trial. The computer platform applies the study design information and the corresponding parameter values to a trained machine learning model to calculate a predicted travel score indicative of a travel burden for the subject. When the travel score fails to satisfy a travel score threshold, the computer platform determines at least one suggestion for adjusting the travel score and the at least one suggestion is output. The computer platform outputs the predicted travel score.

Using machine learning to evaluate patients and control a clinical trial

Methods, systems, and devices supporting adjusting user interfaces based on user usage patterns are described. A page layout server may store an initial page layout for a page corresponding to a data object type, where the initial page layout may be defined by a first user and include a set of user interface components. The page layout server may transmit, to a set of user devices, a first indication of the page for display by the set of user devices according to the initial page layout. The page layout server may track user interactions with the set of user interface components, determine an updated page layout for the page based on an analysis of the tracked user interactions and the initial page layout, and transmit a second indication of the page for display by the set of user devices according to the updated page layout.

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Durham, NC, United States of America

Stephen Carraway