论文标题
通过上下文自动完成的快速，结构化的临床文档
Fast, Structured Clinical Documentation via Contextual Autocomplete
论文作者
论文摘要
我们提出了一种使用学习的自动完成机制来促进半结构临床文档的快速创建的系统。我们通过利用非结构化和结构化医学数据的特征来动态地建议相关的临床概念作为医生起草笔记。通过将我们的体系结构限制为浅的神经网络，我们可以实时提出这些建议。此外，由于我们的算法用来写笔记，因此我们可以自动注释文档，并用从医学词汇表中提取的临床概念的干净标签，使笔记对医师，患者和未来算法更具结构化和可读性。据我们所知，该系统是用于现场医院中部署的临床笔记的唯一基于机器学习的文档实用程序，并且在实际环境中，临床概念的钥匙负担减少了67％。
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation. We dynamically suggest relevant clinical concepts as a doctor drafts a note by leveraging features from both unstructured and structured medical data. By constraining our architecture to shallow neural networks, we are able to make these suggestions in real time. Furthermore, as our algorithm is used to write a note, we can automatically annotate the documentation with clean labels of clinical concepts drawn from medical vocabularies, making notes more structured and readable for physicians, patients, and future algorithms. To our knowledge, this system is the only machine learning-based documentation utility for clinical notes deployed in a live hospital setting, and it reduces keystroke burden of clinical concepts by 67% in real environments.
