Alex Borges Vieira

Caroline de Oliveira Costa Souza Rosa, Marcia Ito, Alex Borges Vieira et al.

The sequence of visits and procedures performed by the patient in the health system, also known as the patient's pathway or trajectory, can reveal important information about the clinical treatment adopted and the health service provided. The rise of electronic health data availability made it possible to assess the pathways of a large number of patients. Nevertheless, some challenges also arose concerning how to synthesize these pathways and how to mine them from the data, fostering a new field of research. The objective of this review is to survey this new field of research, highlighting representation models, mining techniques, methods of analysis, and examples of case studies.

Caroline de Oliveira Costa Souza Rosa, Márcia Ito, Alex Borges Vieira et al.

The automatic discovery of a model to represent the history of encounters of a group of patients with the healthcare system -- the so-called "pathway of patients" -- is a new field of research that supports clinical and organisational decisions to improve the quality and efficiency of the treatment provided. The pathways of patients with chronic conditions tend to vary significantly from one person to another, have repetitive tasks, and demand the analysis of multiple perspectives (interventions, diagnoses, medical specialities, among others) influencing the results. Therefore, modelling and mining those pathways is still a challenging task. In this work, we propose a framework comprising: (i) a pathway model based on a multi-aspect graph, (ii) a novel dissimilarity measurement to compare pathways taking the elapsed time into account, and (iii) a mining method based on traditional centrality measures to discover the most relevant steps of the pathways. We evaluated the framework using the study cases of pregnancy and diabetes, which revealed its usefulness in finding clusters of similar pathways, representing them in an easy-to-interpret way, and highlighting the most significant patterns according to multiple perspectives.

Ronan D. Mendonça, Otávio S. Gomes, Luiz F. M. Vieira et al.

In this paper, we propose a blockchain-based cold chain technology for vaccine cooling track. The COVID-19 pandemic has caused the death of millions of people. An important step towards ending the pandemic is vaccination. Vaccines must be kept under control temperature during the whole process, from fabrication to the hands of the health professionals who will immunize the population. However, there are numerous reports of vaccine loss due to temperature variations, and, currently, people getting vaccinated have no control if their vaccine was kept safe. Blockchain is a technology solution that can provide public and verifiable records. We review the World Health Organization (WHO) cool chain and Blockchain technology. Moreover, we describe current IoT temperature monitoring devices and propose Blockcoldchain to track vaccine cold chain using blockchain, thus proving an unalterable vaccine temperature history. Our experimental results using smart contracts demonstrate the system's feasibility.

Claudio D. T. Barros, Matheus R. F. Mendonça, Alex B. Vieira et al.

Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature evolution, and diffusion. Therefore, several methods for embedding dynamic graphs have been proposed to learn network representations over time, facing novel challenges, such as time-domain modeling, temporal features to be captured, and the temporal granularity to be embedded. In this survey, we overview dynamic graph embedding, discussing its fundamentals and the recent advances developed so far. We introduce the formal definition of dynamic graph embedding, focusing on the problem setting and introducing a novel taxonomy for dynamic graph embedding input and output. We further explore different dynamic behaviors that may be encompassed by embeddings, classifying by topological evolution, feature evolution, and processes on networks. Afterward, we describe existing techniques and propose a taxonomy for dynamic graph embedding techniques based on algorithmic approaches, from matrix and tensor factorization to deep learning, random walks, and temporal point processes. We also elucidate main applications, including dynamic link prediction, anomaly detection, and diffusion prediction, and we further state some promising research directions in the area.