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首页> 外文期刊>BMC Medical Informatics and Decision Making >Measuring diversity in medical reports based on categorized attributes and international classification systems
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Measuring diversity in medical reports based on categorized attributes and international classification systems

机译：根据分类属性和国际分类系统衡量医疗报告中的多样性

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Background Narrative medical reports do not use standardized terminology and often bring insufficient information for statistical processing and medical decision making. Objectives of the paper are to propose a method for measuring diversity in medical reports written in any language, to compare diversities in narrative and structured medical reports and to map attributes and terms to selected classification systems. Methods A new method based on a general concept of f-diversity is proposed for measuring diversity of medical reports in any language. The method is based on categorized attributes recorded in narrative or structured medical reports and on international classification systems. Values of categories are expressed by terms. Using SNOMED CT and ICD 10 we are mapping attributes and terms to predefined codes. We use f-diversities of Gini-Simpson and Number of Categories types to compare diversities of narrative and structured medical reports. The comparison is based on attributes selected from the Minimal Data Model for Cardiology (MDMC). Results We compared diversities of 110 Czech narrative medical reports and 1119 Czech structured medical reports. Selected categorized attributes of MDMC had mostly different numbers of categories and used different terms in narrative and structured reports. We found more than 60% of MDMC attributes in SNOMED CT. We showed that attributes in narrative medical reports had greater diversity than the same attributes in structured medical reports. Further, we replaced each value of category (term) used for attributes in narrative medical reports by the closest term and the category used in MDMC for structured medical reports. We found that relative Gini-Simpson diversities in structured medical reports were significantly smaller than those in narrative medical reports except the "Allergy" attribute. Conclusions Terminology in narrative medical reports is not standardized. Therefore it is nearly impossible to map values of attributes (terms) to codes of known classification systems. A high diversity in narrative medical reports terminology leads to more difficult computer processing than in structured medical reports and some information may be lost during this process. Setting a standardized terminology would help healthcare providers to have complete and easily accessible information about patients that would result in better healthcare.
机译：背景技术叙事医疗报告没有使用标准化的术语，常常带来了不足以进行统计处理和医疗决策的信息。本文的目的是提出一种测量以任何语言编写的医学报告中的多样性的方法，以比较叙述性和结构化医学报告中的多样性，并将属性和术语映射到选定的分类系统。方法提出了一种基于f多样性的一般概念的新方法，用于测量任何语言的医学报告的多样性。该方法基于叙事或结构化医疗报告中记录的分类属性以及国际分类系统。类别的值用术语表示。使用SNOMED CT和ICD 10，我们可以将属性和术语映射到预定义的代码。我们使用基尼-辛普森（Gini-Simpson）和类别数（Number of Categories）类型的f多样性来比较叙述性和结构化医疗报告的多样性。比较是基于从“心脏病最小数据模型”（MDMC）中选择的属性。结果我们比较了110篇捷克叙述性医学报告和1119篇捷克结构性医学报告的多样性。 MDMC的选定分类属性大部分具有不同的类别数量，并且在叙述性报告和结构化报告中使用了不同的术语。我们在SNOMED CT中发现了超过60％的MDMC属性。我们表明，叙述性医疗报告中的属性比结构化医疗报告中的相同属性具有更大的多样性。此外，我们将叙述性医疗报告中用于属性的类别（术语）的每个值替换为最接近的术语，并将MDMC中用于结构化医疗报告的类别替换为最接近的术语。我们发现，结构化医学报告中相对的Gini-Simpson多样性要比叙述性医学报告中的“过敏”属性小得多。结论叙述性医学报告中的术语不规范。因此，几乎不可能将属性（项）的值映射到已知分类系统的代码。叙述性医疗报告术语的高度多样性导致比结构化医疗报告更加困难的计算机处理，并且在此过程中可能会丢失一些信息。设置标准化术语将有助于医疗保健提供者获得有关患者的完整且易于访问的信息，从而改善医疗保健。

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