PDF(Pubmed)
Abstract：
CONCLUSIONS: Analyzing narratives in patients\' medical records using a framework that combines natural language processing (NLP) and machine learning may help uncover the underlying patterns of patients\' visual capabilities and challenges that they are facing and could be useful in analyzing big data in optometric research.
OBJECTIVE: The primary goal of this study was to demonstrate the feasibility of applying a framework that combines NLP and machine learning to analyze narratives in patients\' medical records. To test and validate our framework, we applied it to analyze records of low vision patients and to address two questions: Was there association between patients\' narratives related to activities of daily living and the quality of their vision? Was there association between patients\' narratives related to activities of daily living and their sentiments toward certain \"assistive items\"?
METHODS: Our dataset consisted of 616 records of low vision patients. From patients\' complaint history, we selected multiple keywords that were related to common activities of daily living. Sentences related to each keyword were converted to numerical data using NLP techniques. Machine learning was then applied to classify the narratives related to each keyword into two categories, labeled based on different \"factors of interest\" (acuity, contrast sensitivity, and sentiments of patients toward certain \"assistive items\").
RESULTS: Using our proposed framework, when patients\' narratives related to specific keywords were used as input, our model effectively predicted the categories of different factors of interest with promising performance. For example, we found strong associations between patients\' narratives and their acuity or contrast sensitivity for certain activities of daily living (e.g., \"drive\" in association with acuity and contrast sensitivity).
CONCLUSIONS: Despite our limited dataset, our results show that the proposed framework was able to extract the semantic patterns stored in medical narratives and to predict patients\' sentiments and quality of vision.
OBJECTIVE: The primary goal of this study was to demonstrate the feasibility of applying a framework that combines NLP and machine learning to analyze narratives in patients\' medical records. To test and validate our framework, we applied it to analyze records of low vision patients and to address two questions: Was there association between patients\' narratives related to activities of daily living and the quality of their vision? Was there association between patients\' narratives related to activities of daily living and their sentiments toward certain \"assistive items\"?
METHODS: Our dataset consisted of 616 records of low vision patients. From patients\' complaint history, we selected multiple keywords that were related to common activities of daily living. Sentences related to each keyword were converted to numerical data using NLP techniques. Machine learning was then applied to classify the narratives related to each keyword into two categories, labeled based on different \"factors of interest\" (acuity, contrast sensitivity, and sentiments of patients toward certain \"assistive items\").
RESULTS: Using our proposed framework, when patients\' narratives related to specific keywords were used as input, our model effectively predicted the categories of different factors of interest with promising performance. For example, we found strong associations between patients\' narratives and their acuity or contrast sensitivity for certain activities of daily living (e.g., \"drive\" in association with acuity and contrast sensitivity).
CONCLUSIONS: Despite our limited dataset, our results show that the proposed framework was able to extract the semantic patterns stored in medical narratives and to predict patients\' sentiments and quality of vision.
摘要：
结论：使用结合了自然语言处理（NLP）和机器学习的框架分析患者病历中的叙述可能有助于揭示患者的潜在模式视觉能力和他们面临的挑战，并且可能有助于分析验光研究中的大数据。
目的：本研究的主要目的是证明应用结合NLP和机器学习的框架来分析患者病历中的叙述的可行性。为了测试和验证我们的框架，我们将其用于分析低视力患者的记录并解决两个问题：患者与日常生活活动有关的叙述与视力质量之间是否存在关联？患者与日常生活活动有关的叙述与他们对某些“辅助物品”的情绪之间是否存在关联？方法
：我们的数据集包括616个低视力患者的记录。从患者的投诉历史，我们选择了与日常生活中常见活动相关的多个关键词。使用NLP技术将与每个关键词相关的句子转换为数值数据。然后应用机器学习将与每个关键词相关的叙述分为两类，基于不同的“兴趣因素”(敏锐度，对比敏感度,以及患者对某些“辅助物品”的情感)。
结果：使用我们提出的框架，当患者与特定关键字相关的叙述被用作输入时，我们的模型有效地预测了不同关注因素的类别，表现良好。例如,我们发现患者的叙述与他们对某些日常生活活动的敏锐度或对比敏感度之间有很强的关联(例如，与敏锐度和对比敏感度相关的“驱动”)。
结论：尽管我们的数据集有限，我们的研究结果表明，所提出的框架能够提取存储在医学叙述中的语义模式，并预测患者的情绪和视觉质量。
目的：本研究的主要目的是证明应用结合NLP和机器学习的框架来分析患者病历中的叙述的可行性。为了测试和验证我们的框架，我们将其用于分析低视力患者的记录并解决两个问题：患者与日常生活活动有关的叙述与视力质量之间是否存在关联？患者与日常生活活动有关的叙述与他们对某些“辅助物品”的情绪之间是否存在关联？方法
：我们的数据集包括616个低视力患者的记录。从患者的投诉历史，我们选择了与日常生活中常见活动相关的多个关键词。使用NLP技术将与每个关键词相关的句子转换为数值数据。然后应用机器学习将与每个关键词相关的叙述分为两类，基于不同的“兴趣因素”(敏锐度，对比敏感度,以及患者对某些“辅助物品”的情感)。
结果：使用我们提出的框架，当患者与特定关键字相关的叙述被用作输入时，我们的模型有效地预测了不同关注因素的类别，表现良好。例如,我们发现患者的叙述与他们对某些日常生活活动的敏锐度或对比敏感度之间有很强的关联(例如，与敏锐度和对比敏感度相关的“驱动”)。
结论：尽管我们的数据集有限，我们的研究结果表明，所提出的框架能够提取存储在医学叙述中的语义模式，并预测患者的情绪和视觉质量。
