Wang Y, Coiera E, Magrabi F. Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity? Journal of the American Medical Informatics Association. 2020.

Objective The study sought 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. Materials and Methods Binary support vector machine (SVM) classifier ensembles were trained and validated using balanced datasets of critical incident report texts (n_type = 2860, n_severity = 1160) collected

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Wang Y, Coiera E, Magrabi F. Using convolutional neural networks to identify patient safety incident reports by type and severity. Journal of the American Medical Informatics Association. 2019; 26(12):1600-8.

Objective To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports. Materials and Methods A CNN with word embedding was applied to identify 10 incident types and 4 severity levels. Model training and validation used data sets (n_type = 2860, n_severity = 1160) collected from

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