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Clustering Similar Diagnosis Terms
Stefan Schulz, Akhila Abdulnazar, Markus Kreuzthaler
837 - 838
10.3233/SHTI230284
Research Article
Studies in Health Technology and Informatics
Volume 302: Caring is Sharing – Exploiting the Value in Data for Health and Innovation
Abstract

A large clinical diagnosis list is explored with the goal to cluster syntactic variants. A string similarity heuristic is compared with a deep learning-based approach. Levenshtein distance (LD) applied to common words only (not tolerating deviations in acronyms and tokens with numerals), together with pair-wise substring expansions raised F1 to 13% above baseline (plain LD), with a maximum F1 of 0.71. In contrast, the model-based approach trained on a German medical language model did not perform better than the baseline, not exceeding an F1 value of 0.42.

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