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首页> 外文期刊>Drug safety: An international journal of medical toxicology and drug experience >Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding
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Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding

机译：使用具有双层嵌入的分层经常性神经网络从电子健康记录中的不良药物事件检测

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IntroductionAdverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. The electronic health records (EHRs) of patients in hospitals contain valuable information regarding ADEs and hence are an important source for detecting ADE signals. However, EHR texts tend to be noisy. Yet applying off-the-shelf tools for EHR text preprocessing jeopardizes the subsequent ADE detection performance, which depends on a well tokenized text input.ObjectiveIn this paper, we report our experience with the NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE1.0), which aims to promote deep innovations on this subject. In particular, we have developed rule-based sentence and word tokenization techniques to deal with the noise in the EHR text.MethodsWe propose a detection methodology by adapting a three-layered, deep learning architecture of (1) recurrent neural network [bi-directional long short-term memory (Bi-LSTM)] for character-level word representation to encode the morphological features of the medical terminology, (2) Bi-LSTM for capturing the contextual information of each word within a sentence, and (3) conditional random fields for the final label prediction by also considering the surrounding words. We experiment with different word embedding methods commonly used in word-level classification tasks and demonstrate the impact of an integrated usage of both domain-specific and general-purpose pre-trained word embedding for detecting ADEs from EHRs.ResultsOur system was ranked first for the named entity recognition task in the MADE1.0 challenge, with a micro-averaged F1-score of 0.8290 (official score).ConclusionOur results indicate that the integration of two widely used sequence labeling techniques that complement each other along with dual-level embedding (character level and word level) to represent words in the input layer results in a deep learning architecture that achieves excellent information extraction accuracy for EHR notes.
机译：介绍药物事件（ADE）检测是迈向有效药物检测的重要步骤，并防止由潜在有害的广告引起的未来事件。医院患者的电子健康记录（EHRS）包含有关ades的宝贵信息，因此是检测ADE信号的重要来源。但是，EHR文本往往是嘈杂的。然而，申请现成的EHR文本的工具预处理危险后续的ADE检测性能，这取决于令牌的策略文本Input.Objectivein本文，我们向检测来自电子健康的药物和不良药物事件的NLP挑战的经验报告了我们的经验记录（制定了1.0），旨在促进这一主题的深刻创新。特别是，我们已经开发了基于规则的句子和字标记化技术，以处理EHR文本中的噪声.TheDswe通过调整（1）复发神经网络的三层深度学习架构[双向的深度学习架构提出了检测方法长期内存（Bi-LSTM）]用于字符级字表示，用于编码医疗术语的形态特征，（2）BI-LSTM，用于捕获句子内每个单词的上下文信息，（3）条件考虑周围单词，最终标签预测的随机字段。我们尝试常用于单词级分类任务的不同词嵌入方法，并展示了统一使用的域特定和通用预训练的单词嵌入用于检测来自EHRS的广告的影响.Resultsour系统首先排名命名的实体识别任务在制作的1.0挑战中，微平均f1分数为0.8290（官方分数）.ConclusUsionour结果表明，两种广泛使用的序列标记技术的集成彼此相互补充，以及双层嵌入（字符级别和字级别）表示输入层中的单词导致深度学习架构，以实现EHR注意事项的优异信息提取精度。

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