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Sensitivity of Estimators for Measuring Information Amount in Web-Based Medical Documents

机译:基于Web的医学文档中信息量估计的估计器的敏感性

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Nowadays, communication between patient and doctor during an appointment has changed significantly owning to the opportunity that medical portals provide. Whether or not necessarily appreciated by the doctors, the patients became more aware of the first symptoms’ suggesting a particular disease and the medical procedures that apply as a standard. Estimating amount of reliable factual medical information in a document is carried out by parametrizing space of digital documents and dividing it into subsequent layers that represent distribution of the system responses computed as random variables to a query about medical information. Analyzed are the following attributes: dynamism of decrease of query words numbers in the documents, precision, recall in the metric space layers, their mutual correlation and specifically the amount of reliable medical information in the documents. Sensitivity of estimators is explored in order to determine the final decision about further browsing digital documents of the metric space for more medical information that satisfies the user’s need. For identification of the true positive information in the space layer and then, in each document of this layer, matching of medical terminology with the document contents, is processed following binary Boolean search space model.
机译:如今,由于医疗门户网站提供的机会,预约期间患者和医生之间的通信已发生了巨大变化。无论是否一定要得到医生的赞赏,患者都会更加了解暗示某种疾病的最初症状以及作为标准的医疗程序。通过对数字文档的空间进行参数化并将其划分为多个后续层,以估计文档中可靠的实际医疗信息的数量,这些后续层表示作为对医疗信息查询的随机变量而计算出的系统响应的分布。分析了以下属性:文档中查询词数量减少的动态性,准确性,度量空间层中的查全率,它们之间的相互关系以及文档中可靠医疗信息的数量。为了确定有关进一步浏览度量空间数字文档以获取更多可满足用户需求的医学信息的最终决定,我们探索了估算器的敏感性。为了识别空间层中的真实阳性信息,然后在该层的每个文档中,按照二进制布尔搜索空间模型处理医学术语与文档内容的匹配。

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