ArticleJMIR medical informatics2019
Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach.
Article in JMIR medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Detection of Antithrombotic-Related Bleeding in Older Inpatients: Multicenter Retrospective Study Using Structured and Unstructured Electronic Health Record Data.Journal of medical Internet research · 2026Article
- Natural Language Processing andJMIR medical informatics · 2025Article
- Identification of Major Bleeding Events in Postoperative Patients With Malignant Tumors in Chinese Electronic Medical Records: Algorithm Development and Validation.JMIR formative research · 2025Article
- Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence.Applied clinical informatics · 2023Article
- A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record.Scientific reports · 2022Article
- Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.Drug safety · 2022Article
- Article
- Artificial Intelligence in Endoscopy.Visceral medicine · 2021Review
- Qualifying Certainty in Radiology Reports through Deep Learning-Based Natural Language Processing.AJNR. American journal of neuroradiology · 2021Article
- Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts.JMIR medical informatics · 2021Article
- Deep learning detects and visualizes bleeding events in electronic health records.Research and practice in thrombosis and haemostasis · 2021Article
- Machine Learning in Modeling of Mouse Behavior.Frontiers in neuroscience · 2021Article
- Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers.JMIR medical informatics · 2020Article
- Deep learning in clinical natural language processing: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation.JMIR medical informatics · 2020Article
- Bleeding Entity Recognition in Electronic Health Records: A Comprehensive Analysis of End-to-End Systems.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.PloS one · 2020Article
- Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors.Translational vision science & technology · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundBleeding 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.
objectiveWe aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event.
methodsWe 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.
resultsHCLA 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.
conclusionsBy incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events.
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