\section{Conclusions}
In this paper, we propose ADAPT, a modality-agnostic representation framework designed to operate effectively under missing modalities during both training and testing. Our framework has been challenged on two different tasks targeting the detection of physiological changes, outperforming the current state of the art while showcasing its superiority for handling missing modalities. Extensive ablations indicate the robustness of our method on different scenarios and strategies. Future work includes applications to other medical tasks.

\section*{Acknowledgments}
This work was partially supported by Inria Action Exploratoire {\sc PreMediT} (Precision Medicine using Topology) and the ANR-22-CE39-0016 APATE. 
Additionally, it was partly performed using HPC resources from GENCI-IDRIS (Grant 2023-AD011014747).
