Artificial Intelligence Outperforms Kaplan-Meier Analyses Estimating Survival after Elective Treatment of Abdominal Aortic Aneurysms.

Objective: Long term differences in survival after elective repair of abdominal aortic aneurysms (AAAs) between open surgical repair (OSR) and endovascular aneurysm repair (EVAR) are unclear, and hitherto artificial intelligence has not been used for this purpose. The aim was to compare the precision of survival estimates between the Kaplan-Meier (KM) method and the artificial intelligence derived method Neural Multi-Task Logistic Regression (N-MTLR), and to compare survival estimates as a function of patient age and time since surgery between OSR and EVAR using N-MTLR.

Methods: All AAAs between 2003 and 2018 in Denmark were identified in the Danish vascular registry.

Wind field reconstruction with adaptive random Fourier features.

We investigate the use of spatial interpolation methods for reconstructing the horizontal near-surface wind field given a sparse set of measurements. In particular, random Fourier features is compared with a set of benchmark methods including kriging and inverse distance weighting. Random Fourier features is a linear model approximating the velocity field, with randomly sampled frequencies and amplitudes trained to minimize a loss function.