Learning to Recognize Protected Health Information in Electronic Health Records with Recurrent Neural Network

机译：运用递归神经网络学习识别电子病历中受保护的健康信息

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De-identification in electronic health records is a prerequisite to distribute medical records for further clinical data processing or mining. In this paper, we introduce a framework based on recurrent neural network to solve the de-identification problem, and compare state-of-the-art methods with our framework. It is integrated, which includes records skeleton generation, chunk representation and protected information labeling. We evaluate our framework on three datasets involving two English datasets from i2b2 de-identification challenge and a Chinese dataset we created. To the best of our knowledge, we are the first to apply RNN model to the Chinese de-identification problem. The experimental results indicate that our framework not only achieves high performance but also has strong generalization ability.

机译：电子病历中的取消标识是分发病历以进行进一步的临床数据处理或挖掘的先决条件。在本文中，我们介绍了一个基于递归神经网络的框架来解决去识别问题，并将最先进的方法与我们的框架进行比较。它是集成的，包括记录框架生成，块表示和受保护的信息标记。我们在三个数据集上评估我们的框架，这些数据集涉及来自i2b2去识别挑战的两个英文数据集和我们创建的中文数据集。据我们所知，我们是第一个将RNN模型应用于中文去识别问题的人。实验结果表明，我们的框架不仅实现了高性能，而且具有很强的泛化能力。

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来源
《Natural language understanding and intelligent applications》|2016年|575-582|共8页
会议地点 Kunming(CN)
作者
Kun Li; Yumei Chai; Hongling Zhao; Xiaofei Nan; Yueshu Zhao;
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作者单位

Information Engineering School, Zhengzhou University, Zhengzhou, China;

Information Engineering School, Zhengzhou University, Zhengzhou, China;

Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China;

Information Engineering School, Zhengzhou University, Zhengzhou, China;

Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China,Hospital of Zhengzhou University, Zhengzhou, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
De-identification; Electronic Health Record; Recurrent Neural Network;

机译：取消识别；电子健康记录；递归神经网络;

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2. Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding [J] . Wunnava Susmitha, Qin Xiao, Kakar Tabassum, Drug safety: An international journal of medical toxicology and drug experience . 2019,第1期

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3. Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data: Implications for Temporal Modeling With Respect to Time Before Diagnosis, Data Density, Data Quantity, and Data Type [J] . Robert Chen, Walter F. Stewart, Jimeng Sun, Circulation. Cardiovascular quality and outcomes . 2019,第10期

机译：复发性神经网络，用于早期检测心力衰竭的纵向电子健康记录数据：对诊断前的时间模型，数据密度，数据量和数据类型的时间建模的影响
4. Learning to Recognize Protected Health Information in Electronic Health Records with Recurrent Neural Network [C] . Kun Li, Yumei Chai, Hongling Zhao, International conference on computer processing of oriental languages . 2016

机译：学习通过经常性神经网络识别电子健康记录中的受保护的健康信息
5. You Want Me to Document What? A Qualitative Descriptive Study of ED Nurses' Perceived Role in the Use of Electronic Healthcare Records in a Learning Healthcare System [D] . ?Hewins, Kelly Jo 2020

机译：你想让我记录什么？在学习医疗保健系统中使用电子医疗保健记录中的ED护士在学习中的识别作用的定性描述性研究
6. A qualitative study of interprofessional learning related to electronic health record (EHR) medication reconciliation within a social knowledge networking (SKN) system [O] . Pavani Rangachari, Kevin C Dellsperger, R Karl Rethemeyer 2019

机译：在社会知识网络（SKN）系统中与电子健康记录（EHR）药物和解相关的跨专业学习的定性研究
7. Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach [O] . Rumeng Li, Baotian Hu, Feifan Liu, 2019

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8. Progress in Electronic Health Record Implementation Through HRSA Grants to Health Center Controlled Networks. [R] . 2014

机译：通过HRsa资助健康中心控制网络实现电子健康记录的进展。

Learning to Recognize Protected Health Information in Electronic Health Records with Recurrent Neural Network

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