Lifespan AI - Project C1: Explainable AI: Inference across the Lifespan

最近人工智能(AI)在几乎所有研究领域的崛起都紧随其后的是可解释AI(XAI)方法，它试图解释AI系统和黑盒机器学习模型。事实证明，这些方法在许多研究领域都表现良好，包括在健康数据上的应用。例如，XAI方法用于检测医学成像中的相关像素区域。然而，在寿命健康数据中，我们面临着具有不同数据结构和维度的不同输入数据、长期测量的纵向预测因素和与时间相关的结果。将这些数据集成到人工智能系统中尤其具有挑战性，解释或解释由此产生的模型也同样具有挑战性。在这个项目中，我们通过开发XAI方法来应对这一挑战，该方法可以处理覆盖整个生命周期的数据，包括纵向预测、时间依赖的结果和不同数据源的组合，这些数据随着生命周期的变化而变化。我们既解决了解释问题，即理解模型本身，也解决了解释问题，即在已经训练的模型上使用事后处理方法。对于解释，我们使用可逆的神经网络结构，它直接将网络的输出与其输入联系起来。对于解释，我们发展了基于归因的方法(例如基于梯度的方法)、注意机制和基于损失的方法。此外，针对高维条件变量选择和复杂数据的条件独立性检验，我们发展了一种方法来生成具有归一化流程的寿命健康数据的所谓仿制品。总而言之，我们的目的是解释和解释在研究单位开发的人工智能模型。这对研究单位以及更广泛地说对健康科学至关重要，因为它有助于了解健康和疾病机制的主要目标。这对人工智能的一般领域也很重要，因为我们的方法进步超越了健康数据，例如文本或语音处理。

The recent rise of artificial intelligence (AI) in almost all research fields is closely followed by methods of explainable AI (XAI), which try to explain AI systems and black box machine learning models. These methods proved to perform well in many research fields, including applications on health data. For example, XAI methods are used to detect relevant pixel regions in medical imaging. However, in lifespan health data, we face heterogeneous input data with varying data structures and dimensions, longitudinal predictors measured over long periods of time and time-dependent outcomes. It is particularly challenging to integrate such data into AI systems and it is equally challenging to explain or interpret the resulting models. In this project, we tackle this challenge by developing XAI methods that can handle data covering the whole lifespan, including longitudinal predictors, time-dependent outcomes and combinations of different data sources, which change over the lifespan. We address both interpretation, i.e. understanding a model by itself, and explanation, i.e. using post-hoc methods on already trained models. For interpretation, we use invertible neural network structures, which directly relate the network's output with its input. For explanation, we develop methods based on attributions (e.g. gradient-based), attention mechanisms and loss-based methods. Further, we develop methods to generate so-called knockoffs for lifespan health data with normalizing flows, aiming at high dimensional conditional variable selection and conditional independence testing on such complex data. In summary, we aim at explaining and interpreting the AI models developed in the research unit. That is crucial for the research unit and, more generally, for health science because it contributes to the major goal of understanding health and disease mechanisms. It is also important for the general field of AI because our methodological advancements generalise beyond health data, e.g. to text or speech processing.