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WikiMed and PubMedDS: Two large-scale datasets for medical concept extraction and normalization research

Authors: Vashishth, Shikhar; Newman-Griffis, Denis; Joshi, Rishabh; Dutt, Ritam; Rosé, Carolyn P;

WikiMed and PubMedDS: Two large-scale datasets for medical concept extraction and normalization research

Abstract

Two large-scale, automatically-created datasets of medical concept mentions, linked to the Unified Medical Language System (UMLS). WikiMed Derived from Wikipedia data. Mappings of Wikipedia page identifiers to UMLS Concept Unique Identifiers (CUIs) was extracted by crosswalking Wikipedia, Wikidata, Freebase, and the NCBI Taxonomy to reach existing mappings to UMLS CUIs. This created a 1:1 mapping of approximately 60,500 Wikipedia pages to UMLS CUIs. Links to these pages were then extracted as mentions of the corresponding UMLS CUIs. WikiMed contains: 393,618 Wikipedia page texts 1,067,083 mentions of medical concepts 57,739 unique UMLS CUIs Manual evaluation of 100 random samples of WikiMed found 91% accuracy in the automatic annotations at the level of UMLS CUIs, and 95% accuracy in terms of semantic type. PubMedDS Derived from biomedical literature abstracts from PubMed. Mentions were automatically identified using distant supervision based on Medical Subject Heading (MeSH) headers assigned to the papers in PubMed, and recognition of medical concept mentions using the high-performance scispaCy model. MeSH header codes are included as well as their mappings to UMLS CUIs. PubMedDS contains: 13,197,430 abstract texts 57,943,354 medical concept mentions 44,881 unique UMLS CUIs Comparison with existing manually-annotated datasets (NCBI Disease Corpus, BioCDR, and MedMentions) found 75-90% precision in automatic annotations. Please note this dataset is not a comprehensive annotation of medical concept mentions in these abstracts (only mentions located through distant supervision from MeSH headers were included), but is intended as data for concept normalization research. Due to its size, PubMedDS is distributed as 30 individual files of approximately 1.5 million mentions each. Data format Both datasets use JSON format with one document per line. Each document has the following structure: { "_id": "A unique identifier of each document", "text": "Contains text over which mentions are ", "title": "Title of Wikipedia/PubMed Article", "split": "[Not in PubMedDS] Dataset split: <train/test/valid>", "mentions": [ { "mention": "Surface form of the mention", "start_offset": "Character offset indicating start of the mention", "end_offset": "Character offset indicating end of the mention", "link_id": "UMLS CUI. In case of multiple CUIs, they are concatenated using '|', i.e., CUI1|CUI2|..." }, {} ] } Version history Version Notes 1.0.0 Initial release 1.0.1 Corrected duplication error in WikiMed.zip file

Creation of these datasets was supported in part by National Science Foundation Grants (IIS 1917668 and IIS 1822831), Dow Chemical, and the National Library of Medicine of the National Institutes of Health under award number T15 LM007059.

Related Organizations
Keywords

PubMed, Medical Concept Extraction, Biomedical Information Extraction, Medical Concept Normalization, Natural Language Processing, Wikipedia

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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