Institute of Psychiatry, King's College London, London, UK.
Neuroimage. 2011 May 15;56(2):809-13. doi: 10.1016/j.neuroimage.2010.05.023. Epub 2010 May 17.
There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction.
越来越多的证据表明，将机器学习分类应用于神经影像学测量可能有助于开发精神病学的诊断和预后预测工具。然而，目前的方法并没有产生预测可靠性的度量。了解与特定预测相关的错误风险对于开发基于神经影像学的临床工具至关重要。我们提出了一种通用的概率分类方法，为磁共振成像（MRI）数据生成置信度度量。我们描述了将转导一致性预测器（TCP）应用于 MRI 图像。TCP 生成最有可能的预测和有效的置信度度量，以及给定置信水平的所有可能预测的集合。我们介绍了 TCP 的理论基础，并将 TCP 应用于患者和健康对照者的结构和功能 MRI 数据，以研究抑郁症的诊断和预后预测。我们验证了 TCP 的预测与支持向量机等更标准的机器学习方法一样准确，同时为每个预测提供了有效的置信度度量。
