Design and application of a generic clinical decision support system for multiscale data

Jussi Mattila; Juha Koikkalainen; Arho Virkki; Mark Van Gils; Jyrki Lötjönen

doi:10.1109/TBME.2011.2170986

Design and application of a generic clinical decision support system for multiscale data

Jussi Mattila^*
, Juha Koikkalainen
, Arho Virkki
, Mark Van Gils
, Jyrki Lötjönen

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli › Tieteellinen › vertaisarvioitu

45 Sitaatiot (Scopus)

Abstrakti

Medical research and clinical practice are currently being redefined by the constantly increasing amounts of multiscale patient data. New methods are needed to translate them into knowledge that is applicable in healthcare. Multiscale modeling has emerged as a way to describe systems that are the source of experimental data. Usually, a multiscale model is built by combining distinct models of several scales, integrating, e.g., genetic, molecular, structural, and neuropsychological models into a composite representation. We present a novel generic clinical decision support system, which models a patients disease state statistically from heterogeneous multiscale data. Its goal is to aid in diagnostic work by analyzing all available patient data and highlighting the relevant information to the clinician. The system is evaluated by applying it to several medical datasets and demonstrated by implementing a novel clinical decision support tool for early prediction of Alzheimers disease.

Alkuperäiskieli	Englanti
Artikkeli	6036158
Sivut	234-240
Sivumäärä	7
Julkaisu	IEEE Transactions on Biomedical Engineering
Vuosikerta	59
Numero	1
DOI - pysyväislinkit	https://doi.org/10.1109/TBME.2011.2170986
Tila	Julkaistu - tammik. 2012
Julkaistu ulkoisesti	Kyllä
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Rahoitus

The authors thank participants of project PredictAD, funded partially by the 7th Framework Program by the European Commission under the ICT theme Virtual Physiological Human (Grant Agreement 224328). Data used in preparation of this article were obtained from the Alzheimer’s Disease Neu-roimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wpcontent/uploads/how_to_apply/ ADNI_Authorship_List.pdf. Manuscript received April 8, 2011; revised June 22, 2011 and August 24, 2011; accepted September 26, 2011. Date of publication October 10, 2011; date of current version December 21, 2011. This work was supported in part by the 7th Framework Program by the European Commission (http.//cordis.europa.eu/ist; EU-Grant-224328-PredictAD; From Patient Data to Personalized Healthcare in Alzheimer’s Disease). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). Asterisk indicates corresponding author.

!!ASJC Scopus subject areas

Biomedical Engineering

Pääsy asiakirjaan

10.1109/TBME.2011.2170986

Siteeraa tätä

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title = "Design and application of a generic clinical decision support system for multiscale data",

abstract = "Medical research and clinical practice are currently being redefined by the constantly increasing amounts of multiscale patient data. New methods are needed to translate them into knowledge that is applicable in healthcare. Multiscale modeling has emerged as a way to describe systems that are the source of experimental data. Usually, a multiscale model is built by combining distinct models of several scales, integrating, e.g., genetic, molecular, structural, and neuropsychological models into a composite representation. We present a novel generic clinical decision support system, which models a patients disease state statistically from heterogeneous multiscale data. Its goal is to aid in diagnostic work by analyzing all available patient data and highlighting the relevant information to the clinician. The system is evaluated by applying it to several medical datasets and demonstrated by implementing a novel clinical decision support tool for early prediction of Alzheimers disease.",

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note = "Funding Information: The authors thank participants of project PredictAD, funded partially by the 7th Framework Program by the European Commission under the ICT theme Virtual Physiological Human (Grant Agreement 224328). Data used in preparation of this article were obtained from the Alzheimer{\textquoteright}s Disease Neu-roimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wpcontent/uploads/how\_to\_apply/ ADNI\_Authorship\_List.pdf. Funding Information: Manuscript received April 8, 2011; revised June 22, 2011 and August 24, 2011; accepted September 26, 2011. Date of publication October 10, 2011; date of current version December 21, 2011. This work was supported in part by the 7th Framework Program by the European Commission (http.//cordis.europa.eu/ist; EU-Grant-224328-PredictAD; From Patient Data to Personalized Healthcare in Alzheimer{\textquoteright}s Disease). Data collection and sharing for this project was funded by the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). Asterisk indicates corresponding author.",

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AU - Van Gils, Mark

AU - Lötjönen, Jyrki

N1 - Funding Information: The authors thank participants of project PredictAD, funded partially by the 7th Framework Program by the European Commission under the ICT theme Virtual Physiological Human (Grant Agreement 224328). Data used in preparation of this article were obtained from the Alzheimer’s Disease Neu-roimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wpcontent/uploads/how_to_apply/ ADNI_Authorship_List.pdf. Funding Information: Manuscript received April 8, 2011; revised June 22, 2011 and August 24, 2011; accepted September 26, 2011. Date of publication October 10, 2011; date of current version December 21, 2011. This work was supported in part by the 7th Framework Program by the European Commission (http.//cordis.europa.eu/ist; EU-Grant-224328-PredictAD; From Patient Data to Personalized Healthcare in Alzheimer’s Disease). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). Asterisk indicates corresponding author.

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N2 - Medical research and clinical practice are currently being redefined by the constantly increasing amounts of multiscale patient data. New methods are needed to translate them into knowledge that is applicable in healthcare. Multiscale modeling has emerged as a way to describe systems that are the source of experimental data. Usually, a multiscale model is built by combining distinct models of several scales, integrating, e.g., genetic, molecular, structural, and neuropsychological models into a composite representation. We present a novel generic clinical decision support system, which models a patients disease state statistically from heterogeneous multiscale data. Its goal is to aid in diagnostic work by analyzing all available patient data and highlighting the relevant information to the clinician. The system is evaluated by applying it to several medical datasets and demonstrated by implementing a novel clinical decision support tool for early prediction of Alzheimers disease.

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KW - Clinical diagnosis

KW - decision support systems

KW - software architecture

KW - supervised learning

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ER -

Design and application of a generic clinical decision support system for multiscale data

Abstrakti

Rahoitus

!!ASJC Scopus subject areas

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