Using Participant Similarity for the Classification of Epidemiological Data on Hepatic Steatosis

Tommy Hielscher, M. Spiliopoulou, H. Völzke, J. Kühn
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引用次数: 21

Abstract

Clinical decision support relies on the findings of epidemiological (longitudinal and cross-sectional) studies on predictive features and risk factors for diseases. Such features flow into the diagnostic procedures. Personalized medicine, which aims to optimize clinical decision making by taking individual characteristics of the patients into account, relies on the findings of epidemiology on groups of cohort participants that have common risk factors and exhibit the outcome under study. The identification of such groups requires modeling and exploiting similarity among individuals described through medical tests. In this work, we study how similarity measures for complex objects contribute to class separation for a multifactorial disorder. We present a data preparation, partitioning and classification workflow on cohort participants for the disorder "hepatic steatosis", and report on our findings on classifier performance and identified important features.
利用参与者相似性对肝脂肪变性流行病学资料进行分类
临床决策支持依赖于疾病预测特征和危险因素的流行病学(纵向和横断面)研究结果。这些特征流入诊断程序。个性化医疗的目的是通过考虑患者的个体特征来优化临床决策,它依赖于流行病学对具有共同风险因素并表现出研究结果的队列参与者群体的研究结果。识别这类群体需要建立模型并利用通过医学测试描述的个体之间的相似性。在这项工作中,我们研究了复杂对象的相似性度量如何有助于多因素障碍的类分离。我们提出了“肝脂肪变性”队列参与者的数据准备、划分和分类工作流程,并报告了我们在分类器性能和识别重要特征方面的发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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