While coronary microvascular dysfunction (CMD) is a major cause of ischemia, it is very challenging to diagnose due to lack of CMD-specific screening measures. CMD has been identified as one of the five priority areas of investigation in a 2014 national Research Consensus Conference on Gender-Specific Research in Emergency Care. In this study, we utilized methods from machine learning that leverage structured and unstructured narratives in clinical notes to detect patients with CMD. We have shown that structured data are not sufficient to detect CMD and integrating unstructured data in the computational model boosts the performance significantly.

中文翻译：

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冠状动脉微血管功能障碍患者的分类。

尽管冠状动脉微血管功能障碍（CMD）是局部缺血的主要原因，但由于缺乏CMD特异的筛查措施，诊断非常具有挑战性。在2014年全国急诊中性别特定研究全国共识会议上，CMD被确定为五个优先研究领域之一。在这项研究中，我们利用了机器学习中的方法，这些方法利用了临床笔记中的结构化和非结构化叙述来检测CMD患者。我们已经表明，结构化数据不足以检测CMD，并且将非结构化数据集成到计算模型中可以显着提高性能。