Data Mining

Qualitative Analysis with Health Informatics Data

Bibliographic Data

ID4707478
AuthorsBrian Castellani (0000-0002-8763-8052, Kent State University, Ashtabula, Ohio), J W Peltason (corresponding author), John Castellani (Johns Hopkins University)
Year2003
Volume13
Issue7
Pages1005-1018
Publication date2003-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueQualitative Health Research (JOURNAL)
Journal identifiersISSN: 1049-7323 • E-ISSN: 1552-7557
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/1049732303253523
OpenAlexW14502965
LanguageEN
Citations received6
References cited9

The new computational algorithms emerging in the data mining literature-in particular, the self-organizing map (SOM) and decision tree analysis (DTA)-offer qualitative researchers a unique set of tools for analyzing health informatics data. The uniqueness of these tools is that although they can be used to find meaningful patterns in large, complex quantitative databases, they are qualitative in orientation. To illustrate the utility of these tools, the authors review the two most popular: the SOM and DTA. They provide a basic definition of health informatics, focusing on how data mining assists this field, and apply the SOM and DTA to a hypothetical example to demonstrate what these tools are and how qualitative researchers can use them

Library science · Political science · Artificial Intelligence in Healthcare · Mathematics · Statistical Methods in Epidemiology

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