Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.
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DOI：
10.1093/jamia/ocy068
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发表时间：
2018-10-01
期刊：
影响因子：
--
通讯作者：
Sun J
中科院分区：
文献类型：
--
作者：
Xiao C;Choi E;Sun J
对用于电子健康记录（EHR）数据的深度学习模型进行系统回顾，并说明用于分析不同数据源及其目标应用的各种深度学习架构。我们还强调了正在进行的研究，并确定了建立EHR深度学习模型的开放性挑战。我们在PubMed和Google Scholar中搜索了2010年1月1日至2018年1月31日期间发表的关于使用EHR数据进行深度学习研究的论文。我们根据这些轴进行总结：分析任务的类型，深度学习模型架构的类型，健康数据和任务及其潜在解决方案所带来的特殊挑战，以及评估策略。我们调查和分析了我们发现的98篇文章的多个方面，并确定了以下分析任务：疾病检测/分类，临床事件的顺序预测，概念嵌入，数据增强和EHR数据隐私。然后，我们研究了如何将深度架构应用于这些任务。我们还讨论了建模EHR数据所带来的一些特殊挑战，并回顾了一些流行的方法。最后，我们总结了如何对每个任务进行绩效评估。尽管深度学习在健康分析应用中取得了早期成功，但仍存在许多问题需要解决。我们将详细讨论它们，包括数据和标签的可用性，模型的可解释性和透明度，以及部署的方便性。
To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoing research and identify open challenges in building deep learning models of EHRs. We searched PubMed and Google Scholar for papers on deep learning studies using EHR data published between January 1, 2010, and January 31, 2018. We summarize them according to these axes: types of analytics tasks, types of deep learning model architectures, special challenges arising from health data and tasks and their potential solutions, as well as evaluation strategies. We surveyed and analyzed multiple aspects of the 98 articles we found and identified the following analytics tasks: disease detection/classification, sequential prediction of clinical events, concept embedding, data augmentation, and EHR data privacy. We then studied how deep architectures were applied to these tasks. We also discussed some special challenges arising from modeling EHR data and reviewed a few popular approaches. Finally, we summarized how performance evaluations were conducted for each task. Despite the early success in using deep learning for health analytics applications, there still exist a number of issues to be addressed. We discuss them in detail including data and label availability, the interpretability and transparency of the model, and ease of deployment.
对计算生物学的深度学习。
影响因子：
9.9
作者：
Angermueller C;Pärnamaa T;Parts L;Stegle O
通讯作者：
Stegle O