How CSL Behring Uses Natural Language Processing To Improve MedDRA Coding

3 Pages

Case study of CSL Behring’s effort to reduce manual coding of adverse events into MedDRA. Historically, only 30% of verbatim reports were autocoded; 70% required manual effort, particularly in rare disease cases. Partnering with IQVIA’s Linguamatics NLP platform, autocoding rose to 60% with minimal mismatches. In some cases, NLP-generated codes were more accurate than manual entries. The success prompted plans for a beta version with machine learning to further enhance pharmacovigilance.

Join for free to read

Case Study Leveraging Natural Language Processing in the Enterprise

Case Study Case study: CSL Behring

Case Study CSL Behring

Case Study CSL Behring and CSL Limited

More from IQVIA

Vendor Sheet IQVIA Natural Language Processing (NLP) Data Factory

Case Study How Roche Is Using Natural Language Processing To Gain Patient…

Vendor Sheet IQVIA Natural Language Processing (NLP) Insights Hub

Case Study Insurance Innovation: The Strategic Advantage of Natural…

Case Study

How CSL Behring Uses Natural Language Processing To Improve MedDRA Coding

How CSL Behring Uses Natural Language Processing To Improve MedDRA Coding

You Might Also Like

More from IQVIA