Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 32nd Annual International Conference of the IEEE EMBS 2010 |
| Place of Publication | Piscataway, NJ, USA |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| Pages | 2886-2889 |
| ISBN (Print) | 978-1-4244-4124-2, 978-1-4244-4123-5 |
| DOIs | |
| Publication status | Published - 2010 |
| MoE publication type | A4 Article in a conference publication |
| Event | 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina Duration: 31 Aug 2010 → 4 Sep 2010 |
Conference
| Conference | 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 |
|---|---|
| Abbreviated title | EMBC'10 |
| Country | Argentina |
| City | Buenos Aires |
| Period | 31/08/10 → 4/09/10 |
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Keywords
- Alzheimer`s disease
- feature selection
- decision support
- classification
- data mining
Cite this
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Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease. / van Gils, Mark; Koikkalainen, Juha; Mattila, Jussi; Herukka, Sanna-Kaisa; Lötjönen, Jyrki; Soininen, Hilkka.
Proceedings of the 32nd Annual International Conference of the IEEE EMBS 2010. Piscataway, NJ, USA : IEEE Institute of Electrical and Electronic Engineers , 2010. p. 2886-2889.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease
AU - van Gils, Mark
AU - Koikkalainen, Juha
AU - Mattila, Jussi
AU - Herukka, Sanna-Kaisa
AU - Lötjönen, Jyrki
AU - Soininen, Hilkka
N1 - Project code: 18493
PY - 2010
Y1 - 2010
N2 - Objective and early detection of Alzheimer's disease (AD) is a demanding problem requiring consideration of many modal observations. Potentially, many features could be used to discern between people without AD and those at different stages of the disease. Such features include results from cognitive and memory tests, imaging (MRI, PET) results, cerebral spine fluid data, blood markers etc. However, in order to define an efficient and limited set of features that can be employed in classifiers requires mining of data from many patient cases. In this study we used two databases, ADNI and Kuopio LMCI, to investigate the relative importance of features and their combinations. Optimal feature combinations are to be used in a Clinical Decision Support System that is to be used in clinical AD diagnosis practice.
AB - Objective and early detection of Alzheimer's disease (AD) is a demanding problem requiring consideration of many modal observations. Potentially, many features could be used to discern between people without AD and those at different stages of the disease. Such features include results from cognitive and memory tests, imaging (MRI, PET) results, cerebral spine fluid data, blood markers etc. However, in order to define an efficient and limited set of features that can be employed in classifiers requires mining of data from many patient cases. In this study we used two databases, ADNI and Kuopio LMCI, to investigate the relative importance of features and their combinations. Optimal feature combinations are to be used in a Clinical Decision Support System that is to be used in clinical AD diagnosis practice.
KW - Alzheimer`s disease
KW - feature selection
KW - decision support
KW - classification
KW - data mining
U2 - 10.1109/IEMBS.2010.5626311
DO - 10.1109/IEMBS.2010.5626311
M3 - Conference article in proceedings
SN - 978-1-4244-4124-2
SN - 978-1-4244-4123-5
SP - 2886
EP - 2889
BT - Proceedings of the 32nd Annual International Conference of the IEEE EMBS 2010
PB - IEEE Institute of Electrical and Electronic Engineers
CY - Piscataway, NJ, USA
ER -