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The use of social media in health is emerging as a means of bringing the various actors together with several benefits. In the specific case of cancer disease, these tools can help patients to improve their psychological well-being and their outcomes. As cancer is the cause of a quarter of deaths in Portugal, it is a pressing issue to understand which tools and information both patients and health professionals find most useful to build effective health social media. It was observed that there is a latent need for an oncology social environment, allowing greater well-being for patients and strengthening their relationship with health professionals and institutions, constituting an asset to the services provided. This chapter fills a gap in the bibliography by bringing together the views of both patients and health professionals from several areas, in close collaboration with the Francisco Gentil Portuguese Oncology Institute of Porto, E.P.E. © 2024, IGI Global. All rights reserved.

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This study explores data mining techniques for predicting student dropout in higher education. The research compares different methodological approaches, including alternative algorithms and variations in model specifications. Additionally, we examine the impact of employing either a single model for all university programs or separate models per program. The performance of models with students grouped according to their position on the program study plan was also tested. The training datasets were explored with varying time series lengths (2, 4, 6, and 8 years) and the experiments use academic data from the University of Porto, spanning the academic years from 2012 to 2022. The algorithm that yielded the best results was XGBoost. The best predictions were obtained with models trained with two years of data, both with separate models for each program and with a single model. The findings highlight the potential of data mining approaches in predicting student dropout, offering valuable insights for higher education institutions aiming to improve student retention and success. © 2023 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.

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Traditional historical data analytics is at risk in a world where volatility, uncertainty, complexity, and ambiguity are the new normal. While Streaming Machine Learning (SML) and Time-series Analytics (TSA) attack some aspects of the problem, we still need a comprehensive solution. SML trains models using fewer data and in a continuous/adaptive way relaxing the assumption that data points are identically distributed. TSA considers temporal dependence among data points, but it assumes identical distribution. Every Data Scientist fights this battle with ad-hoc solutions. In this paper, we claim that, due to the temporal dependence on the data, the existing solutions do not represent robust solutions to efficiently and automatically keep models relevant even when changes occur, and real-time processing is a must. We propose a novel and solid scientific foundation for Time-Evolving Analytics from this perspective. Such a framework aims to develop the logical, methodological, and algorithmic foundations for fast, scalable, and resilient analytics.

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