Natural Language Processing in Pharmacoepidemiology: Lessons from the Multi-Source Observational Safety study for Advanced Information Classification Using NLP (MOSAIC-NLP)

When using real-world data (RWD) from electronic health records (EHRs), much important information on confounders and outcomes is contained in the clinical notes. In the MOSAIC-NLP study funded by the U.S. FDA through the Sentinel Innovation Center, we demonstrated the feasibility of applying natural language processing (NLP) to a data set including 17+ million notes from over 100 healthcare systems to extract key information on outcomes and potential confounders. 

This presentation describes the design and lessons learned from the pharmacoepidemiology study, MOSAIC-NLP, which is using linked EHR-claims data to assess the risk of neuropsychiatric adverse events in a cohort of patients with asthma initiating montelukast. Researchers interested in methodological considerations related to incorporating structured data from EHR linked to claims and semi/unstructured EHR data extracted from clinical notes using NLP techniques will benefit from this session. It was presented at the 2024 ISPE Annual Meeting.