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摘要： 电子病历是医院信息化发展的产物, 其中包含了丰富的医疗信息和临床知识, 是辅助临床决策和药物挖掘等的重要资源.因此, 如何高效地挖掘大量电子病历数据中的信息是一个重要的研究课题.近些年来, 随着计算机技术尤其是机器学习以及深度学习的蓬勃发展, 对电子病历这一特殊领域数据的挖掘有了更高的要求.电子病历综述旨在通过对电子病历研究现状的分析来指导未来电子病历文本挖掘领域的发展.具体而言, 综述首先介绍了电子病历数据的特点和电子病历的数据预处理的常用方法；然后总结了电子病历数据挖掘的4个典型任务(医学命名实体识别、关系抽取、文本分类和智能问诊), 并且围绕典型任务介绍了常用的基本模型以及研究人员在任务上的部分探索；最后结合糖尿病和心脑血管疾病2类特定疾病, 对电子病历的现有应用场景做了简单介绍.
https://crad.ict.ac.cn/CN/10.7544/issn1000-1239.2021.20200402
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电子病历文本挖掘研究综述
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The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where there are difficulties in collecting new annotated data. Transfer learning aims to boost the performance of a target learner by applying another related source data. In contrast to the traditional machine learning and data mining techniques, which assume that the training and testing data lie from the same feature space and distribution, transfer learning can handle situations where there is a discrepancy between domains and distributions. These characteristics give the model the potential to utilize the available related source data and extend the underlying knowledge to the target task achieving better performance. This survey paper aims to give a concise review of traditional and current transfer learning settings, existing challenges, and related approaches.
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The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where there are difficulties in collecting new annotated data. Transfer learning aims to boost the performance of a target learner by applying another related source data. In contrast to the traditional machine learning and data mining techniques, which assume that the training and testing data lie from the same feature space and distribution, transfer learning can handle situations where there is a discrepancy between domains and distributions. These characteristics give the model the potential to utilize the available related source data and extend the underlying knowledge to the target task achieving better performance. This survey paper aims to give a concise review of traditional and current transfer learning settings, existing challenges, and related approaches.
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