AI in Medicine

Forecasting individual survival in irregularly sampled patient trajectories

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Abstract

Time series forecasting of patient trajectories plays a critical role in the clinical environment by enabling the prediction of possibly treatment relevant patient events . Clinical data such as imaging studies, surgical records, laboratory measurements or tumor staging provide rich longitudinal information reflecting the progression of disease or treatment response. Modeling these data involves several challenges such as integrating multi-modal data, handling irregularly sampling over time, or managing missing values. Many existing forecasting approaches rely on regularly sampled data and perform poorly when facing irregularly sampled clinical data. Here, we evaluate three different deep learning models for predicting individual six month survival from irregularly sampled lung cancer patient trajectories. Results show that state-of-the-art models can integrate sparse clinical data and benefit from multi-modality, improving forecasting of clinical outcomes despite irregular sampling patterns.

How to Cite:

Sobotka, D., Bogveradze, N., Beer, L., Seeböck, P., Prosch, H. & Langs, G., (2026) “Forecasting individual survival in irregularly sampled patient trajectories”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 28-32.