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. Survival was estimated using the KM and N-MTLR methods, and prediction performance was estimated with the Brier score. RESULTS: 7 912 patients were included in the study, n = 6 569 (83%) men, median age 72 years (range 35 - 92), with a median follow-up time of 45.7 months (range 0 - 120). The two treatment cohorts, OSR n = 5 495 (69%), and EVAR n = 2 417 (31%), differed significantly in patient characteristics. The Brier score for KM increased from 0.044 to 0.244, and for N-MTLR from 0.044 to 0.206, from 90 days to 10 years. The N-MTLR method was more accurate than KM from 90 days to 10 years after surgery, p ≤ .025. N-MTLR demonstrated significant increased probability for survival for OSR in patients aged 58 - 76 years at five years, and 65 - 73 at 10 years after surgery, and the opposite was found for the benefit of EVAR in patients aged 72 - 85 years at one year, 85 - 90 years at five years, and for 85 - 90 year olds at 10 years after surgery. CONCLUSION: N-MTLR outperforms KM for the entire post-operative follow-up time. This N-MTLR model has the potential to render more precise patient specific survival estimates and establish survival differences between subgroups of patients that KM is unable to detect, demonstrated here for different age groups.

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 ß ( x ) = ∑ k = 1 K ß k e i ω k x approximating the velocity field, with randomly sampled frequencies ω k and amplitudes ß k trained to minimize a loss function. We include a physically motivated divergence penalty | ∇ ⋅ ß ( x ) | 2 , as well as a penalty on the Sobolev norm of ß . We derive a bound on the generalization error and a sampling density that minimizes the bound. We then devise an adaptive Metropolis-Hastings algorithm for sampling the frequencies of the optimal distribution. In our experiments, our random Fourier features model outperforms the benchmark models.