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US Patent 12125563 System and method for predicting subject enrollment

Patent 12125563 was granted and assigned to Medidata Solutions on October, 2024 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
Medidata Solutions
Medidata Solutions
1
Current Assignee
Medidata Solutions
Medidata Solutions
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
121255631
Patent Inventor Names
Fanyi Zhang1
Jingshu Liu1
Michael Elashoff1
Hrishikesh Karvir1
Christopher Bound1
Date of Patent
October 22, 2024
1
Patent Application Number
179378991
Date Filed
October 4, 2022
1
Patent Citations
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US Patent 10515099 Medical clinical trial site identification
1
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US Patent 11107559 System and method for identifying one or more investigative sites of a clinical trial and centrally managing data therefrom
1
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US Patent 10740437 Systems and methods for predictive data analytics
1
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US Patent 7085690 Unsupervised machine learning-based mathematical model selection
1
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US Patent 8706537 Remote clinical study site monitoring and data quality scoring
1
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US Patent 10311442 Business methods and systems for offering and obtaining research services
1
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US Patent 10311534 Methods and apparatus for planning and management of clinical trials
1
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US Patent 10395198 Forecasting a time series based on actuals and a plan
1
Patent Primary Examiner
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Vincent Gonzales
1
CPC Code
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G16H 50/50
1
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G16H 50/30
1
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G16H 80/00
1
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G06N 20/00
1
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G16H 10/20
1
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G16H 50/70
1
Patent abstract

A system for predicting subject enrollment for a study includes a time-to-first-enrollment (TTFE) model and a first-enrollment-to-last-enrollment (FELE) model for each site in the study. The TTFE model includes a Gaussian distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression, and the FELE model includes a negative binomial distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression estimation.

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