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Using word-cooccurrence to determine health problems from health-care documents

机译:使用Word-Cooccurrence确定医疗保健文件的健康问题

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This research aims to determine health problems, especially symptom occurrence on the health-care documents for the automatic problem-diagnosis system. This research concerns the actual event of the symptom occurence mostly expressed by verb phrase on a simple sentence. Thus, we apply a word cooccurrence with the symptom concept having the first word as a verb to determine the symptom occurrence on a certain simple sentence. However, the research still contains two main problems, the first problem is the ambiguous word cooccurrence. The second problem is to determine the number of words (starting with verb) to identify word co-occurrence with the symptom concept from the verb phrase after the stop word removal. Therefore, Pearson Correlation was proposed as the solution to these problems. The results of this research can provide the high precision of the health-problem determination.
机译:该研究旨在确定健康问题,特别是对自动问题诊断系统的保健文件的症状发生。这项研究涉及症状发生的实际事件大多表达了一个简单的句子上的动词短语。因此,我们将一句话与症状概念应用于具有第一个单词作为动词的症状概念,以确定某个简单句子的症状发生。但是,研究仍然包含两个主要问题,第一个问题是模糊的词同联。第二个问题是确定在停止单词删除后从动词短语中识别单词共同发生的单词(从动词开始)。因此,提出了Pearson相关性作为对这些问题的解决方案。该研究的结果可以提供健康问题决定的高精度。

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