SapBERT-Based Medical Concept Normalization Using SNOMED CT

被引：2

作者：

Abdulnazar, Akhila ^{[1
,2
]}

Kreuzthaler, Markus ^{[1
]}

Roller, Roland ^{[3
]}

Schulz, Stefan ^{[1
]}

机构：

[1] Med Univ Graz, Inst Med Informat Stat & Documentat, Graz, Austria

[2] Ctr Biomarker Res Med, Graz, Austria

[3] German Res Ctr Artificial Intelligence, Kaiserslautern, Germany

来源：

CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023 | 2023年 / 302卷

关键词：

Medical Concept Mapping; SNOMED CT;

D O I：

10.3233/SHTI230278

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Word vector representations, known as embeddings, are commonly used for natural language processing. Particularly, contextualized representations have been very successful recently. In this work, we analyze the impact of contextualized and non-contextualized embeddings for medical concept normalization, mapping clinical terms via a k-NN approach to SNOMED CT. The non-contextualized concept mapping resulted in a much better performance (F1-score = 0.853) than the contextualized representation (F-1-score = 0.322).

引用

页码：825 / 826

页数：2

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