期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 59, 期 1, 页码 234-240出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2011.2170986
关键词
Clinical diagnosis; decision support systems; software architecture; supervised learning
资金
- European Commission [EU-Grant-224328-PredictAD]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- European Commission under the ICT theme Virtual Physiological Human [224328]
- NATIONAL INSTITUTE ON AGING [R01AG007367, R01AG012101, U01AG024904, R01AG022374] Funding Source: NIH RePORTER
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.
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