Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| 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
- personal health systems
- wellness
- behavioral change
- patient compliance
Cite this
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Personal Health Systems : Opportunities and Barriers for Adoption. / Korhonen, Ilkka; Mattila, Elina M.; van Gils, Mark.
Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE Institute of Electrical and Electronic Engineers , 2010.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Personal Health Systems
T2 - Opportunities and Barriers for Adoption
AU - Korhonen, Ilkka
AU - Mattila, Elina M.
AU - van Gils, Mark
N1 - Project code: 18982
PY - 2010
Y1 - 2010
N2 - Objective and early detection of Alzheimer's disease (AD) is a demanding problem requiring consideration of manymodal 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 manymodal 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 - personal health systems
KW - wellness
KW - behavioral change
KW - patient compliance
U2 - 10.1109/IEMBS.2010.5626309
DO - 10.1109/IEMBS.2010.5626309
M3 - Conference article in proceedings
SN - 978-1-4244-4124-2
SN - 978-1-4244-4123-5
BT - Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology
PB - IEEE Institute of Electrical and Electronic Engineers
ER -