Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, whic...Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.展开更多
电子病历(Electronic medical records,EMR)产生于临床治疗过程,其中命名实体和实体关系反映了患者健康状况,包含了大量与患者健康状况密切相关的医疗知识,因而对它们的识别和抽取是信息抽取研究在医疗领域的重要扩展.本文首先讨论了电...电子病历(Electronic medical records,EMR)产生于临床治疗过程,其中命名实体和实体关系反映了患者健康状况,包含了大量与患者健康状况密切相关的医疗知识,因而对它们的识别和抽取是信息抽取研究在医疗领域的重要扩展.本文首先讨论了电子病历文本的语言特点和结构特点,然后在梳理了命名实体识别和实体关系抽取研究一般思路的基础上,分析了电子病历命名实体识别、实体修饰识别和实体关系抽取研究的具体任务和对应任务的主要研究方法.本文还介绍了相关的共享评测任务和标注语料库以及医疗领域几个重要的词典和知识库等资源.最后对这一研究领域仍需解决的问题和未来的发展方向作了展望.展开更多
基金the Artificial Intelligence Innovation and Development Project of Shanghai Municipal Commission of Economy and Information (No. 2019-RGZN-01081)。
文摘Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.
文摘电子病历(Electronic medical records,EMR)产生于临床治疗过程,其中命名实体和实体关系反映了患者健康状况,包含了大量与患者健康状况密切相关的医疗知识,因而对它们的识别和抽取是信息抽取研究在医疗领域的重要扩展.本文首先讨论了电子病历文本的语言特点和结构特点,然后在梳理了命名实体识别和实体关系抽取研究一般思路的基础上,分析了电子病历命名实体识别、实体修饰识别和实体关系抽取研究的具体任务和对应任务的主要研究方法.本文还介绍了相关的共享评测任务和标注语料库以及医疗领域几个重要的词典和知识库等资源.最后对这一研究领域仍需解决的问题和未来的发展方向作了展望.
