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

32 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
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Volume59
Issue number1
DOIs
Publication statusPublished - 2012
MoE publication typeA1 Journal article-refereed

Fingerprint

Decision support systems
Composite materials

Keywords

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

Cite this

Mattila, Jussi ; Koikkalainen, Juha ; Virkki, Arho ; van Gils, Mark ; Lötjönen, Jyrki. / Design and application of a generic clinical decision support system for multiscale data. In: IEEE Transactions on Biomedical Engineering. 2012 ; Vol. 59, No. 1. pp. 234-240.
@article{c28d0f8bf32940fa8e774e0502d1fd35,
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 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.",
keywords = "Clinical diagnosis, decision support systems, software architecture, supervised learning",
author = "Jussi Mattila and Juha Koikkalainen and Arho Virkki and {van Gils}, Mark and Jyrki L{\"o}tj{\"o}nen",
note = "Project code: PredictAD 18493",
year = "2012",
doi = "10.1109/TBME.2011.2170986",
language = "English",
volume = "59",
pages = "234--240",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "Institute of Electrical and Electronic Engineers IEEE",
number = "1",

}

Design and application of a generic clinical decision support system for multiscale data. / Mattila, Jussi; Koikkalainen, Juha; Virkki, Arho; van Gils, Mark; Lötjönen, Jyrki.

In: IEEE Transactions on Biomedical Engineering, Vol. 59, No. 1, 2012, p. 234-240.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Design and application of a generic clinical decision support system for multiscale data

AU - Mattila, Jussi

AU - Koikkalainen, Juha

AU - Virkki, Arho

AU - van Gils, Mark

AU - Lötjönen, Jyrki

N1 - Project code: PredictAD 18493

PY - 2012

Y1 - 2012

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 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.

AB - 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.

KW - Clinical diagnosis

KW - decision support systems

KW - software architecture

KW - supervised learning

U2 - 10.1109/TBME.2011.2170986

DO - 10.1109/TBME.2011.2170986

M3 - Article

VL - 59

SP - 234

EP - 240

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 1

ER -