Personal Health Systems: Opportunities and Barriers for Adoption

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Print)978-1-4244-4124-2, 978-1-4244-4123-5
DOIs
Publication statusPublished - 2010
MoE publication typeA4 Article in a conference publication
Event32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: 31 Aug 20104 Sep 2010

Conference

Conference32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Abbreviated titleEMBC'10
CountryArgentina
CityBuenos Aires
Period31/08/104/09/10

Fingerprint

Alzheimer Disease
Health
Clinical Decision Support Systems
Data Mining
Early Diagnosis
Spine
Databases
Practice (Psychology)

Keywords

  • personal health systems
  • wellness
  • behavioral change
  • patient compliance

Cite this

Korhonen, I., Mattila, E. M., & van Gils, M. (2010). Personal Health Systems: Opportunities and Barriers for Adoption. In Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/IEMBS.2010.5626309
Korhonen, Ilkka ; Mattila, Elina M. ; van Gils, Mark. / Personal Health Systems : Opportunities and Barriers for Adoption. Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE Institute of Electrical and Electronic Engineers , 2010.
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Korhonen, I, Mattila, EM & van Gils, M 2010, Personal Health Systems: Opportunities and Barriers for Adoption. in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE Institute of Electrical and Electronic Engineers , 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, Buenos Aires, Argentina, 31/08/10. https://doi.org/10.1109/IEMBS.2010.5626309

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 proceedingConference article in proceedingsScientificpeer-review

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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.

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Korhonen I, Mattila EM, van Gils M. Personal Health Systems: Opportunities and Barriers for Adoption. In Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE Institute of Electrical and Electronic Engineers . 2010 https://doi.org/10.1109/IEMBS.2010.5626309