Methods toolbox and infrastructure for predictive analytics

这个WP有一个服务和一个科学组成部分，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，另一个将推动真正的科学模型开发，用于预测内化精神障碍的治疗反应。在服务组件中，我们将1）以所有参与PI都可以访问的方式安全地存储和组织回顾性和前瞻性数据，2）提供硬件，用于使用最先进的机器学习算法（包括深度学习）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于预测分析和机器学习算法的方法工具箱，用于在单个受试者水平上识别（非）响应者。这将针对不同疾病以及跨疾病单独进行，以研究治疗反应是否在个体或跨诊断量表上起作用。我们将系统地比较不同数据域（如临床数据、动态评估、电生理数据和神经影像学数据）的预测价值，并将评估不同的数据融合方法。具体来说，我们将尝试复杂的机器学习技术，如卷积神经网络来分析神经成像数据，并将采用迁移学习来处理相对较低的样本量。

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.