Using convolutional neural networks to identify patient safety incident reports by type and severity.

ABSTRACT:

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 a statewide incident reporting system. Generalizability was evaluated using an independent hospital-level reporting system. CNN architectures were examined by varying layer size and hyperparameters. Performance was evaluated by F score, precision, recall, and compared to binary support vector machine (SVM) ensembles on 3 testing data sets (type/severity: n_benchmark = 286/116, n_original = 444/4837, n_inde

SUBMITTER: Wang Y 

PROVIDER: S-EPMC7647259 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature