Measuring diversity in medical reports based on categorized attributes and international classification systems

<p>Abstract</p> <p>Background</p> <p>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...

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Main Authors: Přečková Petra, Zvárová Jana, Zvára Karel
Format: Article
Language:English
Published: BMC 2012-04-01
Series:BMC Medical Informatics and Decision Making
Online Access:http://www.biomedcentral.com/1472-6947/12/31
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author Přečková Petra
Zvárová Jana
Zvára Karel
author_facet Přečková Petra
Zvárová Jana
Zvára Karel
author_sort Přečková Petra
collection DOAJ
description <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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).</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>
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spelling doaj.art-0488084ae9a940c99a46995f8b5e3b632022-12-22T03:24:46ZengBMCBMC Medical Informatics and Decision Making1472-69472012-04-011213110.1186/1472-6947-12-31Measuring diversity in medical reports based on categorized attributes and international classification systemsPřečková PetraZvárová JanaZvára Karel<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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).</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>http://www.biomedcentral.com/1472-6947/12/31
spellingShingle Přečková Petra
Zvárová Jana
Zvára Karel
Measuring diversity in medical reports based on categorized attributes and international classification systems
BMC Medical Informatics and Decision Making
title Measuring diversity in medical reports based on categorized attributes and international classification systems
title_full Measuring diversity in medical reports based on categorized attributes and international classification systems
title_fullStr Measuring diversity in medical reports based on categorized attributes and international classification systems
title_full_unstemmed Measuring diversity in medical reports based on categorized attributes and international classification systems
title_short Measuring diversity in medical reports based on categorized attributes and international classification systems
title_sort measuring diversity in medical reports based on categorized attributes and international classification systems
url http://www.biomedcentral.com/1472-6947/12/31
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