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首页> 外文期刊>ACM transactions on intelligent systems >Recognition of Patient-Related Named Entities in Noisy Tele-Health Texts
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Recognition of Patient-Related Named Entities in Noisy Tele-Health Texts

机译：嘈杂的远程医疗文本中与患者相关的命名实体的识别

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We explore methods for effectively extracting information from clinical narratives that are captured in a public health consulting phone service called HealthLink. Our research investigates the application of state-of-the-art natural language processing and machine learning to clinical narratives to extract information of interest. The currently available data consist of dialogues constructed by nurses while consulting patients by phone. Since the data are interviews transcribed by nurses during phone conversations, they include a significant volume and variety of noise. When we extract the patient-related information from the noisy data, we have to remove or correct at least two kinds of noise: explicit noise, which includes spelling errors, unfinished sentences, omission of sentence delimiters, and variants of terms, and implicit noise, which includes non-patient information and patient's untrustworthy information. To filter explicit noise, we propose our own biomedical term detectionormalization method: it resolves misspelling, term variations, and arbitrary abbreviation of terms by nurses. In detecting temporal terms, temperature, and other types of named entities (which show patients' personal information such as age and sex), we propose a bootstrapping-based pattern learning process to detect a variety of arbitrary variations of named entities. To address implicit noise, we propose a dependency path-based filtering method. The result of our denoising is the extraction of normalized patient information, and we visualize the named entities by constructing a graph that shows the relations between named entities. The objective of this knowledge discovery task is to identify associations between biomedical terms and to clearly expose the trends of patients' symptoms and concern; the experimental results show that we achieve reasonable performance with our noise reduction methods.
机译：我们探索有效地从称为HealthLink的公共卫生咨询电话服务中捕获的临床叙述中提取信息的方法。我们的研究调查了最先进的自然语言处理和机器学习在临床叙事中的应用，以提取感兴趣的信息。当前可用的数据包括护士在通过电话咨询患者时进行的对话。由于数据是护士在电话交谈中进行的访谈，因此其中包含大量噪声。当我们从嘈杂的数据中提取与患者有关的信息时，我们必须消除或纠正至少两种噪声：显式噪声，包括拼写错误，未完成的句子，省略句子定界符，术语变体和隐式噪声，其中包括非患者信息和患者的不信任信息。为了过滤明显的噪音，我们提出了自己的生物医学术语检测/归一化方法：它解决了护士拼错，术语变化和术语任意缩写的问题。在检测时间术语，温度和其他类型的命名实体（显示患者的个人信息，例如年龄和性别）时，我们提出了一种基于自举的模式学习过程，以检测命名实体的各种任意变化。为了解决隐式噪声，我们提出了一种基于依赖路径的滤波方法。去噪的结果是提取规范化的患者信息，并且我们通过构建一个显示命名实体之间关系的图形来可视化命名实体。这项知识发现任务的目的是识别生物医学术语之间的关联，并清楚地揭示患者症状和忧虑的趋势。实验结果表明，使用降噪方法可以达到合理的性能。

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