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Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach
Open Access Articles
  • Rumeng Li, University of Massachusetts Amherst
  • Baotian Hu, University of Massachusetts Lowell
  • Feifan Liu, University of Massachusetts Medical School
  • Weisong Liu, University of Massachusetts Lowell
  • Francesca Cunningham, Department of Veterans Affairs
  • David D. McManus, University of Massachusetts Medical School
  • Hong Yu, University of Massachusetts Medical School
UMMS Affiliation
Department of Quantitative Health Sciences; Division of Cardiovascular Medicine, Department of Medicine
Publication Date
2019-2-8
Document Type
Article
Abstract

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.

Keywords
  • BiLSTM,
  • autoencoder,
  • bleeding,
  • convolutional neural networks,
  • electronic health record
Rights and Permissions
© Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D McManus, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 02.02.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
DOI of Published Version
10.2196/10788
Source

JMIR Med Inform. 2019 Feb 8;7(1):e10788. doi: 10.2196/10788. Link to article on publisher's site

Related Resources

Link to Article in PubMed

PubMed ID
30735140
Creative Commons License
Creative Commons Attribution 4.0
Citation Information
Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, et al.. "Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach" Vol. 7 Iss. 1 (2019) ISSN: 2291-9694 (Print)
Available at: http://0-works.bepress.com.library.simmons.edu/david_mcmanus/184/