Department of Psychiatry, University Medical Center Utrecht, Rudolf Magnus Institute of Neuroscience, The Netherlands.
Schizophr Res. 2013 Sep;149(1-3):108-11. doi: 10.1016/j.schres.2013.07.002. Epub 2013 Jul 20.
Automatic classification of individuals at increased risk for schizophrenia can become an important screening method that allows for early intervention based on disease markers, if proven to be sufficiently accurate. Conventional classification methods typically consider information from single subjects, thereby ignoring (heritable) features of the person's relatives. In this paper we show that the inclusion of these features can lead to an increase in classification accuracy from 0.54 to 0.72 using a support vector machine model. This inclusion of contextual information is especially useful in diseases where the classification features carry a heritable component.
如果被证明足够准确，那么个体精神分裂症风险增加的自动分类可以成为一种重要的筛选方法，从而可以基于疾病标志物进行早期干预。传统的分类方法通常只考虑单个主体的信息，从而忽略了（可遗传的）个体亲属的特征。在本文中，我们表明，通过使用支持向量机模型，包含这些特征可以将分类准确性从 0.54 提高到 0.72。在分类特征具有遗传成分的疾病中，包含这种上下文信息尤其有用。
