论文标题
用于临床数据预测的多模型比较的视觉分析系统
A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions
论文作者
论文摘要
为了预测患者的未来状态，将机器学习方法应用于医疗数据集的趋势正在增长。尽管其中一些方法达到了高性能，但通过可解释的信息比较和评估不同模型的挑战仍然存在。这种分析可以帮助临床医生改善基于证据的医疗决策。在这项工作中，我们开发了一个视觉分析系统，该系统比较了多个模型的预测标准并评估其一致性。借助我们的系统，用户可以在不同模型的内部标准上产生知识，以及我们如何自信地依靠每个模型对某个患者的预测。通过对公开临床数据集的案例研究，我们证明了视觉分析系统的有效性，以帮助临床医生和研究人员比较和定量评估不同的机器学习方法。
There is a growing trend of applying machine learning methods to medical datasets in order to predict patients' future status. Although some of these methods achieve high performance, challenges still exist in comparing and evaluating different models through their interpretable information. Such analytics can help clinicians improve evidence-based medical decision making. In this work, we develop a visual analytics system that compares multiple models' prediction criteria and evaluates their consistency. With our system, users can generate knowledge on different models' inner criteria and how confidently we can rely on each model's prediction for a certain patient. Through a case study of a publicly available clinical dataset, we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.
