论文标题
一种视觉分析方法，用于构建逻辑回归模型及其在健康记录中的应用
A Visual Analytics Approach to Building Logistic Regression Models and its Application to Health Records
论文作者
论文摘要
多维数据分析在许多领域中变得越来越重要，这主要是由于当前的大量数据可用性以及从中提取知识的需求不断增长。在大多数应用程序中，最终用户的作用对于建立适当的机器学习模型和解释数据中的模式至关重要。在本文中，我们提出了一种开放的统一方法，用于在用户引导的过程中在高维数据集中生成，评估和应用回归模型。该方法是基于公开属性的广泛相关全景图，用户可以选择相关属性以构建和评估一个或多个上下文的预测模型。我们将方法命名为UCREG（以用户为中心的回归）。我们通过将框架应用于Covid-19和其他合成和实际健康记录数据的分析来证明UCREG的有效性和效率。
Multidimensional data analysis has become increasingly important in many fields, mainly due to current vast data availability and the increasing demand to extract knowledge from it. In most applications, the role of the final user is crucial to build proper machine learning models and to explain the patterns found in data. In this paper, we present an open unified approach for generating, evaluating, and applying regression models in high-dimensional data sets within a user-guided process. The approach is based on exposing a broad correlation panorama for attributes, by which the user can select relevant attributes to build and evaluate prediction models for one or more contexts. We name the approach UCReg (User-Centered Regression). We demonstrate effectiveness and efficiency of UCReg through the application of our framework to the analysis of Covid-19 and other synthetic and real health records data.
