Develop an empirical algorithm to automate negative control identification in Sentinel System using the “Data-driven Automated Negative Control Estimation (DANCE)” algorithm
Description
This AI project is designed to solve the problem of optimizing the Data-driven Automated Negative Control Estimation (DANCE) algorithm for real-world implementation in large electronic healthcare database studies. It aims to use plasmode simulation to refine the algorithm's performance and then validate the tailored approach through a multisite test case focused on safety endpoint detection, ensuring the method works effectively across different healthcare data environments.
Detailed example
Electronic Health Record (EHR) data
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Supports the use of plasmode simulation to evaluate and tailor implementation of DANCE in settings relevant to large electronic healthcare database studies and to apply the tailored DANCE algorithm to a test case incorporating a safety endpoint in a multisite implementation, improving FDA's ability to detect drug safety signals.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Electronic Health Record (EHR)