Medical assertion classification in Chinese EMRs using attention enhanced neural network

Zhichang Zhang; Yu Zhang; Tong Zhou; Yali Pang; Zhichang Zhang; Yu Zhang; Tong Zhou; Yali Pang

doi:10.3934/mbe.2019096

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 4: 1966-1977. doi: 10.3934/mbe.2019096

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Research article Special Issues

Medical assertion classification in Chinese EMRs using attention enhanced neural network

College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

Received: 18 December 2018 Accepted: 17 February 2019 Published: 08 March 2019

Electronic medical records (EMRs), such as hospital discharge summaries, contain a wealth of information only expressed in natural language. Automated methods for extracting information from these records must be able to recognize medical concepts in text and their semantic context. A contextual property critical to reason on information from EMRs is the doctor's belief status or assertion of the patient's medical problem. Research on the medical assertion classification (MAC) can establish the foundation for various health data analyses and clinical applications. However, previous MAC studies are mainly based on traditional machine learning methods which mostly require manually constructed features and the original unlabeled data cannot be easily and effectively applied to classification or classification tasks. Furthermore, external medical knowledge such as various medical dictionary bases, which provides rich explain and definition information about medical entity, is rarely utilized in existing neural network models of medical information extraction. In this study, we propose a deep neural network architecture enhanced by medical knowledge attention layer through combining GRU neural network with CNN model to classify the assertion type of medical problem such as disease and symptom in Chinese EMRs. The attention layer in the model is applied to integrate entity representations learned from medical dictionary bases as query for encoding. Experimental results on own manually annotated corpus indicate our approach achieves better performance compared to existing methods.
- Chinese electronic medical record,
- medical entity assertion classification,
- deep learning,
- attention mechanism
Citation: Zhichang Zhang, Yu Zhang, Tong Zhou, Yali Pang. Medical assertion classification in Chinese EMRs using attention enhanced neural network[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 1966-1977. doi: 10.3934/mbe.2019096

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Abstract

Electronic medical records (EMRs), such as hospital discharge summaries, contain a wealth of information only expressed in natural language. Automated methods for extracting information from these records must be able to recognize medical concepts in text and their semantic context. A contextual property critical to reason on information from EMRs is the doctor's belief status or assertion of the patient's medical problem. Research on the medical assertion classification (MAC) can establish the foundation for various health data analyses and clinical applications. However, previous MAC studies are mainly based on traditional machine learning methods which mostly require manually constructed features and the original unlabeled data cannot be easily and effectively applied to classification or classification tasks. Furthermore, external medical knowledge such as various medical dictionary bases, which provides rich explain and definition information about medical entity, is rarely utilized in existing neural network models of medical information extraction. In this study, we propose a deep neural network architecture enhanced by medical knowledge attention layer through combining GRU neural network with CNN model to classify the assertion type of medical problem such as disease and symptom in Chinese EMRs. The attention layer in the model is applied to integrate entity representations learned from medical dictionary bases as query for encoding. Experimental results on own manually annotated corpus indicate our approach achieves better performance compared to existing methods.

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