ArticleJournal of biomedical semantics2019
Combining string and phonetic similarity matching to identify misspelt names of drugs in medical records written in Portuguese.
Article in Journal of biomedical semantics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings.Journal of healthcare informatics research · 2021Article
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2 authors.
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Abstract
backgroundThere is an increasing amount of unstructured medical data that can be analysed for different purposes. However, information extraction from free text data may be particularly inefficient in the presence of spelling errors. Existing approaches use string similarity methods to search for valid words within a text, coupled with a supporting dictionary. However, they are not rich enough to encode both typing and phonetic misspellings.
resultsExperimental results showed a joint string and language-dependent phonetic similarity is more accurate than traditional string distance metrics when identifying misspelt names of drugs in a set of medical records written in Portuguese.
conclusionWe present a hybrid approach to efficiently perform similarity match that overcomes the loss of information inherit from using either exact match search or string based similarity search methods.
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