Toddenroth D (2023)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2023
Publisher: IOS Press
Edited Volumes: Caring is Sharing – Exploiting the Value in Data for Health and Innovation
Series: Studies in Health Technology and Informatics
Book Volume: 302
Pages Range: 808-812
ISBN: 978-1-64368-389-8
DOI: 10.3233/SHTI230271
Many concepts in the medical literature are named after persons. Frequent ambiguities and spelling varieties, however, complicate the automatic recognition of such eponyms with natural language processing (NLP) tools. Recently developed methods include word vectors and transformer models that incorporate context information into the downstream layers of a neural network architecture. To evaluate these models for classifying medical eponymy, we label eponyms and counterexamples mentioned in a convenience sample of 1,079 Pubmed abstracts, and fit logistic regression models to the vectors from the first (vocabulary) and last (contextualized) layers of a SciBERT language model. According to the area under sensitivity-specificity curves, models based on contextualized vectors achieved a median performance of 98.0% in held-out phrases. This outperformed models based on vocabulary vectors (95.7%) by a median of 2.3 percentage points. When processing unlabeled inputs, such classifiers appeared to generalize to eponyms that did not appear among any annotations. These findings attest to the effectiveness of developing domain-specific NLP functions based on pre-trained language models, and underline the utility of context information for classifying potential eponyms.
APA:
Toddenroth, D. (2023). Classifiers of Medical Eponymy in Scientific Texts. In Maria Hägglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindsköld, Parisis Gallos (Eds.), Caring is Sharing – Exploiting the Value in Data for Health and Innovation. (pp. 808-812). IOS Press.
MLA:
Toddenroth, Dennis. "Classifiers of Medical Eponymy in Scientific Texts." Caring is Sharing – Exploiting the Value in Data for Health and Innovation. Ed. Maria Hägglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindsköld, Parisis Gallos, IOS Press, 2023. 808-812.
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