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

Jussi Mattila, Juha Koikkalainen, Arho Virkki, Mark van Gils, Jyrki Lötjönen

    Research output: Contribution to journalArticleScientificpeer-review

    36 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 patient's 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 Alzheimer's disease.
    Original languageEnglish
    Pages (from-to)234-240
    JournalIEEE Transactions on Biomedical Engineering
    Volume59
    Issue number1
    DOIs
    Publication statusPublished - 2012
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Clinical diagnosis
    • decision support systems
    • software architecture
    • supervised learning

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