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 language | English |
|---|---|
| Pages (from-to) | 234-240 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 59 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2012 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- Clinical diagnosis
- decision support systems
- software architecture
- supervised learning