Outcome of very elderly (octogenarians) patients with coronary artery disease, all diagnosed by coronary angiography

Background: Women with ischemic heart disease (IHD) typically present less severe coronary artery atherosclerosis. Despite that, as compared with men, women maintain a worse outcome. This female susceptibility seems to be mainly related to older age of clinical presentation and heavier risk factors burden. Purpose: To investigate whether sex differences exist in the real-world management and clinical outcome of elderly patients with suspected IHD. Methods: Retrospective analysis of IHD elderly (≥ 80 years) patients undergoing coronary angiography for acute coronary syndrome (ACS) or stable angina (SA). Management strategy, including invasive revascularization or a conservative medical approach, and outcome were evaluated. Results: A total of 1420 (41% women; mean age: 83.1 ± 2.8) IHD patients referring for ACS (43%) and SA (57%) were analyzed. Men more likely accessed for SA (59.6% vs 52.5%, p<0.001) whereas ACS was the most frequent reason for angiography in women (28.8% vs 21.5%, p<0.001). No significant sex differences in the burden of obstructive epicardial disease were observed in both ACS and SA patients. No sex disparities in antiplatelet therapy, specifically clopidogrel, were detected. Compared with SA men patients, female ones received more likely a conservative therapy (p=0.049). After a median (IQR) follow-up time of 39.0 (16-71) months, a total of 514 (36%) patients died. No sex differences in cardiac death (p=0.139) was observed. Nevertheless, the Kaplan Meier curves showed a trend in lower all-cause mortality in female group(p=0.093). Conclusions: In the very elderly population, an invasive strategy is superior to a conservative one in terms of survival rate. However, a dilution of the efficacy occurs with increasing age and comorbidities, and for male patients the benefit of the invasive strategy is not clear. Prospective studies are warranted to evaluate the net benefit of an invasive or a conservative approach in older population

Approximated Neighbours MinHash Graph Node Kernel

In this paper, we propose a scalable kernel for nodes in a (huge) graph. In contrast with other state-of-the-art kernels that scale more than quadratically in the number of nodes, our approach scales lin- early in the average out-degree and quadratically in the number of nodes (for the Gram matrix computation). The kernel presented in this paper considers neighbours as sets, thus it ignores edge weights. Nevertheless, experimental results on real-world datasets show promising results

Conditional Constrained Graph Variational Autoencoders for Molecule Design

none3In recent years, deep generative models for graphs have been used to generate new molecules. These models have produced good results, leading to several proposals in the literature. However, these models may have troubles learning some of the complex laws governing the chemical world. In this work, we explore the usage of the histogram of atom valences to drive the generation of molecules in such models. We present Conditional Constrained Graph Variational Autoencoder (CCGVAE), a model that implements this key-idea in a state-of-the-art model, and shows improved results on several evaluation metrics on two commonly adopted datasets for molecule generation.noneDavide Rigoni; Nicolo' Navarin; Alessandro SperdutiRigoni, Davide; Navarin, Nicolo'; Sperduti, Alessandr

Hyper-parameter tuning for graph kernels via multiple kernel learning

Kernelized learning algorithms have seen a steady growth in popularity during the last decades. The procedure to estimate the performances of these kernels in real applications is typical computationally demanding due to the process of hyper-parameter selection. This is especially true for graph kernels, which are computationally quite expensive. In this paper, we study an approach that substitutes the commonly adopted procedure for kernel hyper-parameter selection by a multiple kernel learning procedure that learns a linear combination of kernel matrices obtained by the same kernel with different values for the hyper-parameters. Empirical results on real-world graph datasets show that the proposed methodology is faster than the baseline method when the number of parameter configurations is large, while always maintaining comparable and in some cases superior performances

A Tree-Based Kernel for Graphs

This paper proposes a new tree-based kernel for graphs. Graphs are decomposed into multisets of ordered Directed Acyclic Graphs (DAGs) and a family of kernels computed by application of tree kernels extended to the DAG domain. We focus our attention on the efficient development of one member of this family. A technique for speeding up the computation is given, as well as theoretical bounds and practical evidence of its feasibility. State of the art results on various benchmark datasets prove the effectiveness of our approach