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

Přečková P, Zvárová J, Zvára K - BMC Med Inform Decis Mak (2012)

Bottom Line: We found more than 60% of MDMC attributes in SNOMED CT.We found that relative Gini-Simpson diversities in structured medical reports were significantly smaller than those in narrative medical reports except the "Allergy" attribute.Setting a standardized terminology would help healthcare providers to have complete and easily accessible information about patients that would result in better healthcare.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre of Biomedical Informatics and EuroMISE Center, Institute of Computer Science AS CR, Prague, the Czech Republic. preckova@euromise.cz

ABSTRACT

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.

Show MeSH
Gini-Simpson relative diversities. Gini-Simpson relative diversities in narrative (X) and structured (O) medical reports.
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Figure 1: Gini-Simpson relative diversities. Gini-Simpson relative diversities in narrative (X) and structured (O) medical reports.

Mentions: There were many missing values in attributes of narrative medical reports. We have suspected that these are non-recorded negative findings. We can see that except the Gini-Simpson relative diversity for the "Allergy" attribute, all calculated Gini-Simpson relative diversities in structured medical reports were significantly smaller at the 5% level (p < 0.05) than in narrative medical reports. The difference for the Allergy attribute is not significant at the 5% level. Statistical tests were performed using Z statistics with standardized normal distribution based on estimates of Gini-Simpson diversity. Figure 1 displays Gini-Simpson relative diversities of selected attributes categorized according to MDMC in narrative medical reports (X) and in structured medical reports (O). However, we assume that differences in diversities estimated from narrative and structured medical reports are caused also by many missing observations in narrative reports.


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

Přečková P, Zvárová J, Zvára K - BMC Med Inform Decis Mak (2012)

Gini-Simpson relative diversities. Gini-Simpson relative diversities in narrative (X) and structured (O) medical reports.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3344690&req=5

Figure 1: Gini-Simpson relative diversities. Gini-Simpson relative diversities in narrative (X) and structured (O) medical reports.
Mentions: There were many missing values in attributes of narrative medical reports. We have suspected that these are non-recorded negative findings. We can see that except the Gini-Simpson relative diversity for the "Allergy" attribute, all calculated Gini-Simpson relative diversities in structured medical reports were significantly smaller at the 5% level (p < 0.05) than in narrative medical reports. The difference for the Allergy attribute is not significant at the 5% level. Statistical tests were performed using Z statistics with standardized normal distribution based on estimates of Gini-Simpson diversity. Figure 1 displays Gini-Simpson relative diversities of selected attributes categorized according to MDMC in narrative medical reports (X) and in structured medical reports (O). However, we assume that differences in diversities estimated from narrative and structured medical reports are caused also by many missing observations in narrative reports.

Bottom Line: We found more than 60% of MDMC attributes in SNOMED CT.We found that relative Gini-Simpson diversities in structured medical reports were significantly smaller than those in narrative medical reports except the "Allergy" attribute.Setting a standardized terminology would help healthcare providers to have complete and easily accessible information about patients that would result in better healthcare.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre of Biomedical Informatics and EuroMISE Center, Institute of Computer Science AS CR, Prague, the Czech Republic. preckova@euromise.cz

ABSTRACT

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.

Show MeSH