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Data-Driven Identification of Clinical Real-World Expressions Linked to ICD
Amila Kugic, Bastian Pfeifer, Stefan Schulz, Markus Kreuzthaler
827 - 828
10.3233/SHTI230279
Research Article
Studies in Health Technology and Informatics
Volume 302: Caring is Sharing – Exploiting the Value in Data for Health and Innovation
Abstract

A semi-structured clinical problem list containing ∼1.9 million de-identified entries linked to ICD-10 codes was used to identify closely related real-world expressions. A log-likelihood based co-occurrence analysis generated seed-terms, which were integrated as part of a k-NN search, by leveraging SapBERT for the generation of an embedding representation.

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