通过上下文自动完成快速、结构化的临床文档
Fast, Structured Clinical Documentation via Contextual Autocomplete
摘要：
我们提出了一个系统，使用学习自动完成机制，以促进快速创建半结构化的临床文件。我们动态地建议相关的临床概念，作为一个医生起草一份说明，利用功能，从非结构化和结构化的医疗数据。通过将我们的架构限制为浅层神经网络，我们能够实时的提出这些建议。此外，当我们的算法用于编写注释时，我们可以使用从医学词汇表中提取的临床概念的干净标签自动注释文档，使注释对于医生，患者和未来的算法来说更具结构性和可读性。据我们所知，该系统是唯一一个基于机器学习的文档工具，用于在现场医院环境中部署的临床笔记，它在真实环境中将临床概念的负担减少了67%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
