Methods toolbox and infrastructure for predictive analytics

这个WP有一个服务和一个科学部分，第一个将提供关于数据和模型交换以及高性能计算的硬件和软件的基础设施基础，另一个将推动真正的科学模型开发，用于预测内在化精神障碍的治疗反应。在服务组成部分中，我们将1)以所有参与的PI都可以访问的方式安全地存储和组织回顾和预期数据，以及2)提供硬件，以使用包括深度学习在内的最先进的机器学习算法来处理复杂的生物医学数据。在科学部分，我们将开发一个依赖预测分析和机器学习算法的方法工具箱，用于在单一学科水平上识别(非)应答者。这将分别针对不同的疾病以及跨疾病进行，以调查治疗反应是在个体还是交叉诊断范围内运作。我们将系统地比较不同数据域的预测价值，如临床数据、动态评估、电生理数据和神经成像数据，并将评估不同的数据融合方法。具体地说，我们将试验复杂的机器学习技术，如用于分析神经成像数据的卷积神经网络，并将使用转移学习来处理相对较小的样本量。

This WP has a service and a science component, the first will provide an infrastructural basis regarding hard- and software for data and model exchange as well as high-performance computing, the other will drive genuine scientific model development for prediction of treatment response in internalizing mental disorders. Within the service component, we will 1) securely store and organize retrospective and prospective data in a way that all participating PIs have access and 2) provide hardware for processing complex biomedical data with state-of-the art machine learning algorithms including deep learning. Within the science component, we will develop a methods toolbox relying on predictive analytics and machine learning algorithms for identifying (non-) responders on a single-subject level. This will be done separately for the different disorders as well as across disorders to investigate if treatment response operates on an individual or cross-diagnostic scale. We will systematically compare the predictive value of different data domains, such as clinical data, ambulatory assessment, electrophysiological data, and neuroimaging data, and will evaluate different methods for data fusion. Specifically, we will experiment with sophisticated machine learning techniques such as convolutional neural networks for analyzing neuroimaging data and will employ transfer learning for dealing with comparatively low sample sizes.