Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease

Mark van Gils, Juha Koikkalainen, Jussi Mattila, Sanna-Kaisa Herukka, Jyrki Lötjönen, Hilkka Soininen

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

7 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 32nd Annual International Conference of the IEEE EMBS 2010
Place of PublicationPiscataway, NJ, USA
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages2886-2889
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
Biomarkers
Clinical Decision Support Systems
Data Mining
Early Diagnosis
Spine
Databases
Practice (Psychology)

Keywords

  • Alzheimer`s disease
  • feature selection
  • decision support
  • classification
  • data mining

Cite this

van Gils, M., Koikkalainen, J., Mattila, J., Herukka, S-K., Lötjönen, J., & Soininen, H. (2010). Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease. In Proceedings of the 32nd Annual International Conference of the IEEE EMBS 2010 (pp. 2886-2889). Piscataway, NJ, USA: IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/IEMBS.2010.5626311
van Gils, Mark ; Koikkalainen, Juha ; Mattila, Jussi ; Herukka, Sanna-Kaisa ; Lötjönen, Jyrki ; Soininen, Hilkka. / Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease. Proceedings of the 32nd Annual International Conference of the IEEE EMBS 2010. Piscataway, NJ, USA : IEEE Institute of Electrical and Electronic Engineers , 2010. pp. 2886-2889
@inproceedings{5c83c3812692480e9ea0b716b07041c7,
title = "Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease",
abstract = "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.",
keywords = "Alzheimer`s disease, feature selection, decision support, classification, data mining",
author = "{van Gils}, Mark and Juha Koikkalainen and Jussi Mattila and Sanna-Kaisa Herukka and Jyrki L{\"o}tj{\"o}nen and Hilkka Soininen",
note = "Project code: 18493",
year = "2010",
doi = "10.1109/IEMBS.2010.5626311",
language = "English",
isbn = "978-1-4244-4124-2",
pages = "2886--2889",
booktitle = "Proceedings of the 32nd Annual International Conference of the IEEE EMBS 2010",
publisher = "IEEE Institute of Electrical and Electronic Engineers",
address = "United States",

}

van Gils, M, Koikkalainen, J, Mattila, J, Herukka, S-K, Lötjönen, J & Soininen, H 2010, Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease. in Proceedings of the 32nd Annual International Conference of the IEEE EMBS 2010. IEEE Institute of Electrical and Electronic Engineers , Piscataway, NJ, USA, pp. 2886-2889, 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.2010.5626311

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 proceedingConference article in proceedingsScientificpeer-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 -

van Gils M, Koikkalainen J, Mattila J, Herukka S-K, Lötjönen J, Soininen H. Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease. In 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 https://doi.org/10.1109/IEMBS.2010.5626311