Peterson Kevin J, Liu Hongfang
Division of Information Management and Analytics, Mayo Clinic, Rochester, MN.
Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:497-506. eCollection 2020.
An important function of the patient record is to effectively and concisely communicate patient problems. In many cases, these problems are represented as short textual summarizations and appear in various sections of the record including problem lists, diagnoses, and chief complaints. While free-text problem descriptions effectively capture the clinicians' intent, these unstructured representations are problematic for downstream analytics. We present an automated approach to converting free-text problem descriptions into structured Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) expressions. Our methods focus on incorporating new advances in deep learning to build formal semantic representations of summary level clinical problems from text. We evaluate our methods against current approaches as well as against a large clinical corpus. We find that our methods outperform current techniques on the important relation identification sub-task of this conversion, and highlight the challenges of applying these methods to real-world clinical text.
患者记录的一项重要功能是有效且简洁地传达患者问题。在许多情况下，这些问题以简短的文本摘要形式呈现，并出现在记录的各个部分，包括问题列表、诊断结果和主要症状。虽然自由文本问题描述有效地捕捉了临床医生的意图，但这些非结构化表示对于下游分析来说存在问题。我们提出了一种自动方法，将自由文本问题描述转换为结构化的医学系统命名法 - 临床术语（SNOMED CT）表达式。我们的方法专注于融入深度学习的新进展，以从文本中构建总结级临床问题的形式语义表示。我们将我们的方法与当前方法以及一个大型临床语料库进行评估。我们发现，在这种转换的重要关系识别子任务上，我们的方法优于当前技术，并突出了将这些方法应用于实际临床文本的挑战。
