Schlegel Daniel R, Gordon Kate, Gaudioso Carmelo, Peleg Mor
SUNY Oswego, Oswego, NY, USA.
Roswell Park Cancer Institute, Buffalo, NY, USA.
AMIA Annu Symp Proc. 2020 Mar 4;2019:784-793. eCollection 2019.
Computational representations of the semantic knowledge embedded within clinical practice guidelines (CPGs) may be a significant aid in creating computer interpretable guidelines (CIGs). Formalizing plain text CPGs into CIGs manually is a laborious and burdensome task, even using CIG tools and languages designed to improve the process. Natural language understanding (NLU) systems perform automated reading comprehension, parsing text and using reasoning to convert syntactic information from unstructured text into semantic information. Influenced by successful systems used in other domains, we present the architecture for a system which uses NLU approaches to create semantic representations of entire CPGs. In the future, these representations may be used to generate CIGs.
临床实践指南（CPG）中嵌入的语义知识的计算表示，可能对创建计算机可解释指南（CIG）有很大帮助。即使使用旨在改进这一过程的CIG工具和语言，将纯文本CPG手动形式化为CIG也是一项艰巨而繁重的任务。自然语言理解（NLU）系统执行自动阅读理解，解析文本并运用推理将非结构化文本中的句法信息转换为语义信息。受其他领域中成功系统的影响，我们提出了一种系统架构，该系统使用NLU方法来创建整个CPG的语义表示。未来，这些表示可用于生成CIG。
