NDLI: Interpreting PET Scans by Structured Patient Data: A Data Mining Case Study in Dementia Research

Content Provider	IEEE Xplore Digital Library
Author	Hapfelmeier, A. Schmidt, J. Mueller, M. Kramer, S. Perneczky, R. Kurz, A. Drzezga, A.
Copyright Year	2008
Description	Author affiliation: Nuklearmed. Klinik, Tech. Univ. Munchen, Munich (Drzezga, A.) \|\| Inst. fur Inf., Tech. Univ. Munchen, Garching (Hapfelmeier, A.; Schmidt, J.; Mueller, M.; Kramer, S.) \|\| Klinik u. Poliklinik fur Psychiatrie und Psychotherapie, Tech. Univ. Munchen, Munich (Perneczky, R.; Kurz, A.)
Abstract	One of the goals of medical research in the area of dementia is to correlate images of the brain with other variables, for instance, demographic information or outcomes of clinical tests. The usual approach is to select a subset of patients based on such variables and analyze the images associated with those patients. In this paper, we apply data mining techniques to take the opposite approach: We start with the images and explain the differences and commonalities in terms of the other variables. In the first step, we cluster PET scans of patients to form groups sharing similar features in brain metabolism. To the best of our knowledge, it is the first time ever that clustering is applied to whole PET scans. In the second step, we explain the clusters by relating them to non-image variables. To do so, we employ RSD, an algorithm for relational subgroup discovery, with the cluster membership of patients as target variable. Our results enable interesting interpretations of differences in brain metabolism in terms of demographic and clinical variables. The approach was implemented and tested on an exceptionally large pre-existing data collection of patients with different types of dementia. It comprises 10 GB of image data from 454 PET scans, and 42 variables from psychological and demographical data organized in 11 relations of a relational database. We believe that explaining medical images in terms of other variables (patient records, demographic information, etc.) is a challenging new and rewarding area for data mining research.
Starting Page	213
Ending Page	222
File Size	631492
Page Count	10
File Format	PDF
ISBN	9780769535029
ISSN	15504786
DOI	10.1109/ICDM.2008.128
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Positron emission tomography Data mining Dementia Demography Biomedical imaging Biochemistry Medical tests Image analysis Clustering algorithms Testing structured data clustering PET subgroup discovery alzheimer's disease dementia brain neuro imaging
Content Type	Text
Resource Type	Article

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