Even after early repair of aortic coarctation (AoCo), life expectancy is reduced due to complications such as hypertension. Invasive diagnostic catheterization is used to evaluate peak-to-peak pressure gradients (PGpp) across the CoAo. Clinically significant PGppare those greater than 20 mmHg under resting conditions, in which case the patient is referred for a second intervention to repair the CoAo. In this study, we demonstrate the feasibility of using Physics-Informed Neural Networks (PINNs) to predict PGppin patients with AoCo non-invasively, based on images obtained from cardiac magnetic resonance imaging. We analyzed a group of 3 patients with CoAo under resting and pharmacological stress conditions. We were able to obtain PGppvalues very close to the actual values obtained by diagnostic catheterization, with an absolute error and average percentage error of 0.57 mmHg and 8.29% for the resting condition, and 4.13 mmHg and 8.63% for the pharmacological stress condition. Our method also successfully identified the only patient who presented a clinically significant PGppunder resting conditions, with differences of less than 1 mmHg.

This paper presents an ingestion procedure into an interoperable repository called ALPACS (Access to Local Picture Archiving and Communication Systems). ALPACS serves clinical and hospital users who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY 1.0. This paper shows the automated procedure for data ingestion from the medical imaging provider into the ALPACS repository. The data ingestion procedure was successfully applied from the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) by applying a pseudo-anonymization algorithm at the source and respecting the privacy of sensitive patient data. The transfer is done using international communication standards for health systems, allowing the replication of the procedure for other medical imaging provider institutions.

Process Mining (PM) focuses on monitoring and optimizing long-running business processes by examining their execution event logs (usually complex and heterogeneous) to obtain insights and enable data-driven decisions. Several Machine Learning (ML) techniques have been recently proposed to exploit these logs as learning datasets and enable examination of past events and predict future ones, but their black-box nature makes hard for human analysts to interpret their results and recognize the key parts of input data. Attention mechanisms (AM) is an ML technique that does address these shortcomings, but it has been little used for PM. This article describes the design, results and findings of a systematic literature review of attention mechanisms for PM. We addressed three research questions: (a) for which applications are AM used? (b) which kinds of AM are used? and (c) how are AM combined with other ML techniques? An initial search yield 73 papers, and inclusion/exclusion criteria left sixteen, published between 2017 and 2023. Key finding are that: (1) the most common application is sequence prediction, (2) most studies combine global and item-wise attention, added as layers after an encoder generates the continuous representation, and (3) emerging research topics include anomaly detection and data representation. This study shows that attention mechanisms can help process analysts to get some sense of interpretability, and showcases the bright potential for process mining of attention mechanisms, which paradoxically have received little attention themselves.

Predicting the emergence of links in large evolving networks is a difficult task with many practical applications. Recently, the Science4cast competition has illustrated this challenge presenting a network of 64.000 AI concepts and asking the participants to predict which topics are going to be researched together in the future. In this paper, we present a solution to this problem based on a new family of deep learning approaches, namely Graph Neural Networks.The results of the challenge show that our solution is competitive even if we had to impose severe restrictions to obtain a computationally efficient and parsimonious model: ignoring the intrinsic dynamics of the graph and using only a small subset of the nodes surrounding a target link. Preliminary experiments presented in this paper suggest the model is learning two related, but different patterns: the absorption of a node by a sub-graph and union of more dense sub-graphs. The model seems to excel at recognizing the first type of pattern.

We explore a technique to learn Support Vector Models (SVMs) when training data is partitioned among several data sources. The basic idea is to consider SVMs which can be reduced to Minimal Enclosing Ball (MEB) problems in an feature space. Computation of such SVMs can be efficiently achieved by finding a core-set for the image of the data in the feature space. Our main result is that the union of local core-sets provides a close approximation to a global core-set from which the SVM can be recovered. The method requires hence a single pass through each source of data in order to compute local core-sets and then to recover the SVM from its union. Extensive simulations in small and large datasets are presented in order to evaluate its classification accuracy, transmission efficiency and global complexity, comparing its results with a widely used single-pass heuristic to learn standard SVMs.