We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Medication-rights detection using incident reports: A natural language processing and deep neural network approach.
- Authors
Wong, Zoie Shui-Yee; So, HY; Kwok, Belinda SC; Lai, Mavis WS; Sun, David TF
- Abstract
Medication errors often occurred due to the breach of medication rights that are the right patient, the right drug, the right time, the right dose and the right route. The aim of this study was to develop a medication-rights detection system using natural language processing and deep neural networks to automate medication-incident identification using free-text incident reports. We assessed the performance of deep neural network models in classifying the Advanced Incident Reporting System reports and compared the models' performance with that of other common classification methods (including logistic regression, support vector machines and the decision-tree method). We also evaluated the effects on prediction outcomes of several deep neural network model settings, including number of layers, number of neurons and activation regularisation functions. The accuracy of the models was measured at 0.9 or above across model settings and algorithms. The average values obtained for accuracy and area under the curve were 0.940 (standard deviation: 0.011) and 0.911 (standard deviation: 0.019), respectively. It is shown that deep neural network models were more accurate than the other classifiers across all of the tested class labels (including wrong patient, wrong drug, wrong time, wrong dose and wrong route). The deep neural network method outperformed other binary classifiers and our default base case model, and parameter arguments setting generally performed well for the five medication-rights datasets. The medication-rights detection system developed in this study successfully uses a natural language processing and deep-learning approach to classify patient-safety incidents using the Advanced Incident Reporting System reports, which may be transferable to other mandatory and voluntary incident reporting systems worldwide.
- Subjects
DATABASE management; DECISION trees; FISHER exact test; MEDICATION errors; NATURAL language processing; ARTIFICIAL neural networks; PATIENT safety; PUBLIC health laws; RESEARCH funding; DATA mining; WORKFLOW; LOGISTIC regression analysis; RECEIVER operating characteristic curves; SUPPORT vector machines; DATA analysis software; DESCRIPTIVE statistics; DEEP learning
- Publication
Health Informatics Journal, 2020, Vol 26, Issue 3, p1777
- ISSN
1460-4582
- Publication type
Article
- DOI
10.1177/1460458219889798