Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach

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...

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Veröffentlicht in:	JMIR medical informatics Jg. 7; H. 1; S. e10788
Hauptverfasser:	Li, Rumeng, Hu, Baotian, Liu, Feifan, Liu, Weisong, Cunningham, Francesca, McManus, David D, Yu, Hong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Canada JMIR Publications 08.02.2019
Schlagworte:	Anticoagulants Automation Classification Data collection Deep learning Electronic health records Machine translation Neural networks Original Paper Support vector machines autoencoder bleeding convolutional neural networks BiLSTM electronic health record
ISSN:	2291-9694, 2291-9694
Online-Zugang:	Volltext
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