Previously submitted to: JMIR Medical Informatics (no longer under consideration since Aug 12, 2022)
Date Submitted: Jul 10, 2022
Open Peer Review Period: Jul 6, 2022 - Aug 31, 2022
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Automatic classification of nursing adverse events: Based on hybrid neural network model
ABSTRACT
Background:
Nursing adverse events are nursing accidents, errors and nursing activities with certain impact Which occur in the course of carrying out medical care behaviors. It is usually characterized by high incidence, high impact and serious consequences. At present, the content reported by the hospital nursing adverse event system is mostly unstructured text data, lacking reasonable and clear classification, difficult to analyze manually, with many human factors and the phenomenon of artificially lowering the event level.
Objective:
To automatically classify the occurrence level of nursing adverse events according to the filling contents, and to explore the application of natural language processing technology in nursing big data.
Methods:
We used neural network models to study the problem of predicting the occurrence level of nursing adverse events. Then, we proposed a hybrid neural network model. The model implements the transformation from text to dynamic word vector of nursing adverse events by BERT, and uses CNN and LSTM-Attention to extract the features of text and fuse the structured data reported by nursing adverse events. Finally, the classification prediction of the occurrence level of nursing adverse events is achieved.
Results:
To evaluate the proposed method, we constructed a dataset with 13,265 Chinese records of nursing adverse events reported by a large hospital in past five years. The experimental results showed that the proposed hybrid neural network model obtained an F-value of 84.2% in this task.
Conclusions:
The comparative results of different models demonstrate the effectiveness of our model. The model outperforms the traditional classification model and is able to predict the occurrence level of nursing adverse event records.
Citation
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