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首页> 外文期刊>JMIR Medical Informatics >Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach
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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

机译：使用循环神经网络自动编码器增强的卷积神经网络模型检测电子病历中的出血事件：深度学习方法

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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.
机译：背景出血事件是常见且至关重要的，可能会导致大量发病和死亡。抗凝治疗患者的出血事件高发与心血管疾病有关。及时准确地检测出血事件对于防止严重后果至关重要。由于出血事件通常在临床笔记中进行描述，因此从电子健康记录（EHR）笔记中自动检测出血事件可能会改善药物安全性监测和药物警戒性。目的我们旨在开发一种自然语言处理（NLP）系统，以自动对EHR注释语句是否包含出血事件进行分类。方法我们专家注释了878篇EHR笔记（76,577个句子和562,630个单词标记），以识别句子级别的出血事件。该带注释的语料库用于训练和验证我们的NLP系统。我们开发了创新的混合卷积神经网络（CNN）和长短期记忆（LSTM）自动编码器（HCLA）模型，该模型将CNN架构与双向LSTM（BiLSTM）自动编码器模型集成在一起，以利用大量未标记的EHR数据。结果HCLA在接收器工作特性曲线（0.957）和F1分数（0.938）下达到了最佳区域，以识别句子是否包含出血事件，从而超过了强大的基线支持向量机以及其他CNN和自动编码器模型。结论通过结合监督的CNN模型和预训练的不受监督的BiLSTM自动编码器，HCLA在检测出血事件方面获得了高性能。

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