Ensemble Based Approach for Time Series Classification in Metabolomics

Michael Netzer, Friedrich Hanser, Marc Breit, K.M. Weinberger, Christian Baumgartner, Daniel Baumgarten

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Buch/BerichtForschungBegutachtung

Abstract

BACKGROUND: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare. OBJECTIVES: The aim of this work is to analyze and classify differences in metabolite time series data between groups of individuals regarding their athletic activity. METHODS: We propose a new ensemble-based 2-tier approach to classify metabolite time series data. The first tier uses polynomial fitting to generate a class prediction for each metabolite. An induced classifier (k-nearest-neighbor or naïve bayes) combines the results to produce a final prediction. Metabolite levels of 47 individuals undergoing a cycle ergometry test were measured using mass spectrometry. RESULTS: In accordance with our previous work the statistical results indicate strong changes over time. We found only small but systematic differences between the groups. However, our proposed stacking approach obtained a mean accuracy of 78% using 10-fold cross-validation. CONCLUSION: Our proposed classification approach allows a considerable classification performance for time series data with small differences between the groups.

Originalspracheenglisch
TiteldHealth 2019 - From eHealth to dHelath
UntertitelProceedings of the 13th Health Informatics Meets Digital Health Conference
Redakteure/-innenDieter Hayn, Alphons Eggerth, Günter Schreier
Herausgeber (Verlag)IOS Press
Seiten89-96
Seitenumfang8
ISBN (Print)978-1-61499-974-4
DOIs
PublikationsstatusVeröffentlicht - 2019
VeranstaltungdHealth 2019 - Schönbrunn Palace, Wien, Österreich
Dauer: 28 Mär 201929 Mär 2019
http://www.ehealth2017.at

Publikationsreihe

NameStudies in health technology and informatics
Band260

Konferenz

KonferenzdHealth 2019
LandÖsterreich
OrtWien
Zeitraum28/03/1929/03/19
Internetadresse

Fingerprint

Metabolomics
Metabolites
Time series
Ergometry
Informatics
Sports
Mass spectrometry
Learning systems
Mass Spectrometry
Classifiers
Health
Polynomials

Schlagwörter

    ASJC Scopus subject areas

    • !!Health Information Management
    • !!Health Informatics
    • !!Biomedical Engineering

    Fields of Expertise

    • Human- & Biotechnology

    Dies zitieren

    Netzer, M., Hanser, F., Breit, M., Weinberger, K. M., Baumgartner, C., & Baumgarten, D. (2019). Ensemble Based Approach for Time Series Classification in Metabolomics. in D. Hayn, A. Eggerth, & G. Schreier (Hrsg.), dHealth 2019 - From eHealth to dHelath: Proceedings of the 13th Health Informatics Meets Digital Health Conference (S. 89-96). (Studies in health technology and informatics; Band 260). IOS Press. https://doi.org/10.3233/978-1-61499-971-3-89

    Ensemble Based Approach for Time Series Classification in Metabolomics. / Netzer, Michael; Hanser, Friedrich; Breit, Marc; Weinberger, K.M.; Baumgartner, Christian; Baumgarten, Daniel.

    dHealth 2019 - From eHealth to dHelath: Proceedings of the 13th Health Informatics Meets Digital Health Conference. Hrsg. / Dieter Hayn; Alphons Eggerth; Günter Schreier. IOS Press, 2019. S. 89-96 (Studies in health technology and informatics; Band 260).

    Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Buch/BerichtForschungBegutachtung

    Netzer, M, Hanser, F, Breit, M, Weinberger, KM, Baumgartner, C & Baumgarten, D 2019, Ensemble Based Approach for Time Series Classification in Metabolomics. in D Hayn, A Eggerth & G Schreier (Hrsg.), dHealth 2019 - From eHealth to dHelath: Proceedings of the 13th Health Informatics Meets Digital Health Conference. Studies in health technology and informatics, Bd. 260, IOS Press, S. 89-96, dHealth 2019, Wien, Österreich, 28/03/19. https://doi.org/10.3233/978-1-61499-971-3-89
    Netzer M, Hanser F, Breit M, Weinberger KM, Baumgartner C, Baumgarten D. Ensemble Based Approach for Time Series Classification in Metabolomics. in Hayn D, Eggerth A, Schreier G, Hrsg., dHealth 2019 - From eHealth to dHelath: Proceedings of the 13th Health Informatics Meets Digital Health Conference. IOS Press. 2019. S. 89-96. (Studies in health technology and informatics). https://doi.org/10.3233/978-1-61499-971-3-89
    Netzer, Michael ; Hanser, Friedrich ; Breit, Marc ; Weinberger, K.M. ; Baumgartner, Christian ; Baumgarten, Daniel. / Ensemble Based Approach for Time Series Classification in Metabolomics. dHealth 2019 - From eHealth to dHelath: Proceedings of the 13th Health Informatics Meets Digital Health Conference. Hrsg. / Dieter Hayn ; Alphons Eggerth ; Günter Schreier. IOS Press, 2019. S. 89-96 (Studies in health technology and informatics).
    @inbook{979b6eb036bc41a4857be40cd1f9dd4b,
    title = "Ensemble Based Approach for Time Series Classification in Metabolomics",
    abstract = "BACKGROUND: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare. OBJECTIVES: The aim of this work is to analyze and classify differences in metabolite time series data between groups of individuals regarding their athletic activity. METHODS: We propose a new ensemble-based 2-tier approach to classify metabolite time series data. The first tier uses polynomial fitting to generate a class prediction for each metabolite. An induced classifier (k-nearest-neighbor or na{\"i}ve bayes) combines the results to produce a final prediction. Metabolite levels of 47 individuals undergoing a cycle ergometry test were measured using mass spectrometry. RESULTS: In accordance with our previous work the statistical results indicate strong changes over time. We found only small but systematic differences between the groups. However, our proposed stacking approach obtained a mean accuracy of 78{\%} using 10-fold cross-validation. CONCLUSION: Our proposed classification approach allows a considerable classification performance for time series data with small differences between the groups.",
    keywords = "biomarkers, classification, kinetics, time series",
    author = "Michael Netzer and Friedrich Hanser and Marc Breit and K.M. Weinberger and Christian Baumgartner and Daniel Baumgarten",
    year = "2019",
    doi = "10.3233/978-1-61499-971-3-89",
    language = "English",
    isbn = "978-1-61499-974-4",
    series = "Studies in health technology and informatics",
    publisher = "IOS Press",
    pages = "89--96",
    editor = "Dieter Hayn and Alphons Eggerth and G{\"u}nter Schreier",
    booktitle = "dHealth 2019 - From eHealth to dHelath",
    address = "Netherlands",

    }

    TY - CHAP

    T1 - Ensemble Based Approach for Time Series Classification in Metabolomics

    AU - Netzer, Michael

    AU - Hanser, Friedrich

    AU - Breit, Marc

    AU - Weinberger, K.M.

    AU - Baumgartner, Christian

    AU - Baumgarten, Daniel

    PY - 2019

    Y1 - 2019

    N2 - BACKGROUND: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare. OBJECTIVES: The aim of this work is to analyze and classify differences in metabolite time series data between groups of individuals regarding their athletic activity. METHODS: We propose a new ensemble-based 2-tier approach to classify metabolite time series data. The first tier uses polynomial fitting to generate a class prediction for each metabolite. An induced classifier (k-nearest-neighbor or naïve bayes) combines the results to produce a final prediction. Metabolite levels of 47 individuals undergoing a cycle ergometry test were measured using mass spectrometry. RESULTS: In accordance with our previous work the statistical results indicate strong changes over time. We found only small but systematic differences between the groups. However, our proposed stacking approach obtained a mean accuracy of 78% using 10-fold cross-validation. CONCLUSION: Our proposed classification approach allows a considerable classification performance for time series data with small differences between the groups.

    AB - BACKGROUND: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare. OBJECTIVES: The aim of this work is to analyze and classify differences in metabolite time series data between groups of individuals regarding their athletic activity. METHODS: We propose a new ensemble-based 2-tier approach to classify metabolite time series data. The first tier uses polynomial fitting to generate a class prediction for each metabolite. An induced classifier (k-nearest-neighbor or naïve bayes) combines the results to produce a final prediction. Metabolite levels of 47 individuals undergoing a cycle ergometry test were measured using mass spectrometry. RESULTS: In accordance with our previous work the statistical results indicate strong changes over time. We found only small but systematic differences between the groups. However, our proposed stacking approach obtained a mean accuracy of 78% using 10-fold cross-validation. CONCLUSION: Our proposed classification approach allows a considerable classification performance for time series data with small differences between the groups.

    KW - biomarkers

    KW - classification

    KW - kinetics

    KW - time series

    UR - https://graz.pure.elsevier.com/en/publications/ensemble-based-approach-for-time-series-classification-in-metabolomics(979b6eb0-36bc-41a4-857b-e40cd1f9dd4b).html

    UR - http://www.scopus.com/inward/record.url?scp=85066474556&partnerID=8YFLogxK

    U2 - 10.3233/978-1-61499-971-3-89

    DO - 10.3233/978-1-61499-971-3-89

    M3 - Chapter

    SN - 978-1-61499-974-4

    T3 - Studies in health technology and informatics

    SP - 89

    EP - 96

    BT - dHealth 2019 - From eHealth to dHelath

    A2 - Hayn, Dieter

    A2 - Eggerth, Alphons

    A2 - Schreier, Günter

    PB - IOS Press

    ER -