Ipenza Juan Carlos Carbajal, Romero Noemi Maritza Lapa, Loreto Melina, Júnior Nivan Ferreira, Comba João Luiz Dihl
Instituto de Informática, UFRGS, Brazil.
Centro de Informática, UFPE, Brazil.
Appl Soft Comput. 2022 Jul;124:109093. doi: 10.1016/j.asoc.2022.109093. Epub 2022 Jun 3.
COVID-19 is responsible for the deaths of millions of people around the world. The scientific community has devoted its knowledge to finding ways that reduce the impact and understand the pandemic. In this work, the focus is on analyzing electronic health records for one of the largest public healthcare systems globally, the Brazilian public healthcare system called (SUS). SUS collected more than 42 million flu records in a year of the pandemic and made this data publicly available. It is crucial, in this context, to apply analysis techniques that can lead to the optimization of the health care resources in SUS. We propose QDS-COVID, a visual analytics prototype for creating insights over SUS records. The prototype relies on a state-of-the-art datacube structure that supports slicing and dicing exploration of charts and Choropleth maps for all states and municipalities in Brazil. A set of analysis questions drives the development of the prototype and the construction of case studies that demonstrate the potential of the approach. The results include comparisons against other studies and feedback from a medical expert.
新冠病毒导致全球数百万人死亡。科学界致力于运用其知识来寻找减轻影响和理解这场大流行的方法。在这项工作中，重点是分析全球最大的公共医疗系统之一——巴西公共医疗系统（SUS）的电子健康记录。在大流行的一年里，SUS收集了超过4200万份流感记录，并将这些数据公开。在这种情况下，应用能够优化SUS医疗资源的分析技术至关重要。我们提出了QDS-COVID，这是一个用于对SUS记录进行洞察分析的可视化分析原型。该原型依赖于一种先进的数据立方体结构，支持对巴西所有州和市的图表和分级统计图进行切片和切块探索。一组分析问题推动了原型的开发以及案例研究的构建，这些案例研究展示了该方法的潜力。结果包括与其他研究的比较以及医学专家的反馈。
