DocumentCode
2938432
Title
Similarity retrieval of cardiac reports
Author
Syeda-Mahmood, Tanveer
Author_Institution
IBM Almaden Res. Center, San Jose, CA, USA
fYear
2010
fDate
Aug. 31 2010-Sept. 4 2010
Firstpage
1135
Lastpage
1141
Abstract
Mining medical reports can reveal important information correlating diagnosis with raw measurements helping in decision support. In this paper we address the problem of finding similar measurement reports for aiding clinical decision support. Specifically, we present a new approach to generating document class models for measurement reports using a multi-scale feature-value kernel. The class models serve as natural feature selection mechanism as well as indexes to large report collections. A document retrieval algorithm based on document class models is presented to enable similarity retrieval of pre-diagnosed reports. Collaborative filtering-guided assembly of associated disease labels is used to achieve clinical decision support.
Keywords
data mining; diseases; medical information systems; cardiac reports; clinical decision support; collaborative filtering-guided assembly; disease labels; document retrieval algorithm; medical reports; multiscale feature-value kernel; natural feature selection mechanism; pre-diagnosed reports; Databases; Diseases; Feature extraction; Histograms; Kernel; Mathematical model; Training; California; Cardiovascular Diseases; Data Mining; Database Management Systems; Decision Support Systems, Clinical; Electronic Health Records; Forms and Records Control; Humans; Medical Record Linkage; Natural Language Processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location
Buenos Aires
ISSN
1557-170X
Print_ISBN
978-1-4244-4123-5
Type
conf
DOI
10.1109/IEMBS.2010.5627132
Filename
5627132
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