Open AccessUsing convolutional neural networks to identify patient safety incident reports by type and severity
Author(s) -
Ying Wang,
Enrico Coiera,
Farah Magrabi
Publication year - 2019
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocz146
Subject(s) - generalizability theory , convolutional neural network , support vector machine , computer science , artificial intelligence , hyperparameter , benchmark (surveying) , incident report , f1 score , binary classification , machine learning , pattern recognition (psychology) , data mining , statistics , mathematics , computer security , geodesy , geography
To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports.
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