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Assessing the Readability of Medical Documents: A Ranking Approach

机译：评估医疗文件的可读性：一种排名方法

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Background The use of electronic health record (EHR) systems with patient engagement capabilities, including viewing, downloading, and transmitting health information, has recently grown tremendously. However, using these resources to engage patients in managing their own health remains challenging due to the complex and technical nature of the EHR narratives. Objective Our objective was to develop a machine learning–based system to assess readability levels of complex documents such as EHR notes. Methods We collected difficulty ratings of EHR notes and Wikipedia articles using crowdsourcing from 90 readers. We built a supervised model to assess readability based on relative orders of text difficulty using both surface text features and word embeddings. We evaluated system performance using the Kendall coefficient of concordance against human ratings. Results Our system achieved significantly higher concordance (.734) with human annotators than did a baseline using the Flesch-Kincaid Grade Level, a widely adopted readability formula (.531). The improvement was also consistent across different disease topics. This method’s concordance with an individual human user’s ratings was also higher than the concordance between different human annotators (.658). Conclusions We explored methods to automatically assess the readability levels of clinical narratives. Our ranking-based system using simple textual features and easy-to-learn word embeddings outperformed a widely used readability formula. Our ranking-based method can predict relative difficulties of medical documents. It is not constrained to a predefined set of readability levels, a common design in many machine learning–based systems. Furthermore, the feature set does not rely on complex processing of the documents. One potential application of our readability ranking is personalization, allowing patients to better accommodate their own background knowledge.
机译：背景技术最近，具有查看功能，包括查看，下载和传输健康信息的具有患者参与功能的电子健康记录（EHR）系统的使用已大大增加。然而，由于EHR叙述的复杂性和技术性，利用这些资源来吸引患者管理自己的健康仍然具有挑战性。目标我们的目标是开发一种基于机器学习的系统，以评估复杂文档（如EHR注释）的可读性。方法我们采用众包的方式从90位读者中收集了EHR注释和Wikipedia文章的难度等级。我们建立了一个监督模型，使用表面文字功能和单词嵌入，根据文字难度的相对顺序评估可读性。我们使用肯德尔（Kendall）一致性系数与人类评级的比较来评估系统性能。结果我们的系统与人类注释器的一致性（.734）远高于使用Flesch-Kincaid等级水平（广泛使用的可读性公式（.531））的基线。在不同疾病主题上的改善也是一致的。此方法与单个人类用户的评分的一致性也高于不同人类注释者之间的一致性（.658）。结论我们探索了自动评估临床叙述的可读性水平的方法。我们的基于排名的系统使用简单的文本功能和易于学习的词嵌入，胜过了广泛使用的可读性公式。我们基于排名的方法可以预测医疗文档的相对难度。它不限于一组预定义的可读性级别，这是许多基于机器学习的系统中的常见设计。此外，功能集不依赖于文档的复杂处理。可读性排名的一种潜在应用是个性化，使患者能够更好地适应自己的背景知识。

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