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SHR Neuro Cancer Cardio Lipid Metab Microb

Schulz, S; Abdulnazar, A; Kreuzthaler, M.
Clustering Similar Diagnosis Terms.
Stud Health Technol Inform. 2023; 302: 837-838. Doi: 10.3233/SHTI230284
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Leading authors Med Uni Graz: Schulz Stefan
Co-authors Med Uni Graz: Kreuzthaler Markus Eduard; Kuppassery Abdulnazar Akhila Naz
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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.
Find related publications in this database (using NLM MeSH Indexing): Natural Language Processing - administration & dosage; Language - administration & dosage; Electronic Health Records - administration & dosage; Records - administration & dosage; Cluster Analysis - administration & dosage