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Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid Stoffw Microb

Kugic, A; Pfeifer, B; Schulz, S; Kreuzthaler, M.
Data-Driven Identification of Clinical Real-World Expressions Linked to ICD.
Stud Health Technol Inform. 2023; 302: 827-828. Doi: 10.3233/SHTI230279
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Führende Autor*innen der Med Uni Graz

Kugic Amila

Co-Autor*innen der Med Uni Graz

Kreuzthaler Markus Eduard

Pfeifer Bastian

Schulz Stefan

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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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