FE5: Incorporate a range of frequently used engineering features from EHRs into the Sentinel common data model in the Sentinel EHR and claims linked Data Partner network
Description
This AI project is designed to solve the problem of extracting valuable clinical information trapped in unstructured free-text fields within electronic health records. It aims to create a systematic feature engineering approach that can convert narrative clinical notes and text data into structured, analyzable formats for pharmacoepidemiologic research and drug safety surveillance.
Detailed example
NLP for EHR unstructured data extraction
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 Natural Language Processing (NLP) to extract information on five specific medical concepts from Electronic Health Record (EHR) data and make available in the Sentinel Common Data Model for future drug safety studies, enhancing FDA's surveillance capabilities.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Free-text data from the commercial and development network EHR-claims