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Semantic representation of reported measurements in radiology

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2016
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Title
Semantic representation of reported measurements in radiology
Published in
BMC Medical Informatics and Decision Making, January 2016
DOI 10.1186/s12911-016-0248-9
Pubmed ID
Authors

Heiner Oberkampf, Sonja Zillner, James A. Overton, Bernhard Bauer, Alexander Cavallaro, Michael Uder, Matthias Hammon

Abstract

In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time. We developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect. The proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation. The developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Israel 1 2%
United States 1 2%
Brazil 1 2%
Unknown 38 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 22%
Researcher 8 20%
Student > Master 5 12%
Student > Doctoral Student 4 10%
Student > Bachelor 2 5%
Other 6 15%
Unknown 7 17%
Readers by discipline Count As %
Computer Science 10 24%
Engineering 5 12%
Medicine and Dentistry 5 12%
Psychology 2 5%
Biochemistry, Genetics and Molecular Biology 2 5%
Other 6 15%
Unknown 11 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 January 2016.
All research outputs
#5,259,121
of 7,028,853 outputs
Outputs from BMC Medical Informatics and Decision Making
#741
of 895 outputs
Outputs of similar age
#218,447
of 317,380 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#31
of 33 outputs
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