This study in the Journal of the American Medical Informatics Association aimed to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity. UMLS was compared with results produced by bag-of-words (BOW) classifiers on three testing datasets. The authors found that UMLS-based semantic classifiers were more effective in identifying incidents by type and extreme-risk events than classifiers using bag-of-words (BOW) features.
Can Unified Medical Language System-based semantic representation improve automated identification of patient safety incident reports by type and severity? (October 2020) https://pubmed.ncbi.nlm.nih.gov/32574362/
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