Lifespan AI - successful kick-off meeting

In Lifespan AI, sensitive data is used in compliance with ethical and data protection regulations to advance machine learning (ML) and deep learning (DL) models. The aim is to gain causal insights in order to uncover the causes of complex diseases and optimize prevention strategies. Last Thursday, all those involved in the project were able to exchange ideas for the first time and agree on the common goals.

Lifespan AI - these are the goals

The Lifespan AI work program consists of six projects grouped into three themes that pursue the Lifespan AI vision from different perspectives: Data and Methods (D), Models and Interpretation (M), and Inference and Causality (C). D1 will advance DL strategies to explore and process long-term temporal changes based on the integration of high-dimensional data from multiple sources; D2 will combine neural networks and mixed-effects models to predict individual health trajectories over the life course; M1 will develop "normalizing flow" methods to infer joint distributions and conditional densities for health data; M2 will create a cognitive digital twin from everyday human activities to predict change across age groups; C1 will develop time-adaptive, explainable AI methods for recurrent neural networks and event times; and C2 will derive a framework for causal discovery in longitudinal studies, combining different datasets and accounting for non-linearities.

Further information on the project can be found here.