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JMIR Med Inform. 2019 Feb 8;7(1):e10788. doi: 10.2196/10788.

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

Author information

1
College of Information and Computer Science, University of Massachusetts Amherst, Amherst, MA, United States.
2
Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, United States.
3
Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States.
4
Department of Veterans Affairs, Center for Medication Safety, Hines, IL, United States.
5
Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.
6
Center for Healthcare Organization and Implementation Research, Bedford Veterans Affairs Medical Center, Bedford, MA, United States.

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

PMID:
30735140
DOI:
10.2196/10788
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