多尺度数据通用临床决策支持系统的设计与应用。
Design and application of a generic clinical decision support system for multiscale data.
机构信息
VTT Technical Research Centre of Finland, Tampere, Finland.
出版信息
IEEE Trans Biomed Eng. 2012 Jan;59(1):234-40. doi: 10.1109/TBME.2011.2170986. Epub 2011 Oct 10.
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.
摘要
医学研究和临床实践正受到多尺度患者数据不断增加的重新定义。需要新的方法将其转化为适用于医疗保健的知识。多尺度建模已成为描述实验数据来源系统的一种方法。通常，通过将几个尺度的不同模型组合在一起，例如，将遗传、分子、结构和神经心理学模型整合到一个综合表示中，来构建多尺度模型。我们提出了一种新颖的通用临床决策支持系统，该系统可从异构多尺度数据中对患者的疾病状态进行统计建模。其目标是通过分析所有可用的患者数据并向临床医生突出显示相关信息来辅助诊断工作。该系统通过将其应用于多个医疗数据集进行评估，并通过实现一种用于早期预测阿尔茨海默病的新型临床决策支持工具来展示。
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