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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Vydané v:	JMIR medical informatics Ročník 7; číslo 1; s. e10788
Hlavní autori:	Li, Rumeng, Hu, Baotian, Liu, Feifan, Liu, Weisong, Cunningham, Francesca, McManus, David D, Yu, Hong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Canada JMIR Publications 08.02.2019
Predmet:	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
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