TY  - JOUR
AU  - Li, Rumeng
AU  - Hu, Baotian
AU  - Liu, Feifan
AU  - Liu, Weisong
AU  - Cunningham, Francesca
AU  - McManus, David D
AU  - Yu, Hong
PY  - 2019
DA  - 2019/02/08
TI  - Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach
JO  - JMIR Med Inform
SP  - e10788
VL  - 7
IS  - 1
KW  - autoencoder
KW  - BiLSTM
KW  - bleeding
KW  - convolutional neural networks
KW  - electronic health record
AB  - Background: Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance. Objective: We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event. Methods: We expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data. Results: HCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models. Conclusions: By incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events. 
SN  - 2291-9694
UR  - http://medinform.jmir.org/2019/1/e10788/
UR  - https://doi.org/10.2196/10788
UR  - http://www.ncbi.nlm.nih.gov/pubmed/30735140
DO  - 10.2196/10788
ID  - info:doi/10.2196/10788
ER  -