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

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

45 Citations (Scopus)

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.

Original language	English
Article number	6036158
Pages (from-to)	234-240
Number of pages	7
Journal	IEEE Transactions on Biomedical Engineering
Volume	59
Issue number	1
DOIs	https://doi.org/10.1109/TBME.2011.2170986
Publication status	Published - Jan 2012
Externally published	Yes
Publication type	A1 Journal article-refereed

Funding

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.

Keywords

Clinical diagnosis
decision support systems
software architecture
supervised learning

ASJC Scopus subject areas

Biomedical Engineering

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10.1109/TBME.2011.2170986

Cite this

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

Abstract

Funding

Keywords

ASJC Scopus subject areas

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