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Vis Inf
Vis Inf  2020, Vol. 4 Issue (2): 122-131    DOI: 10.1016/j.visinf.2020.04.005
论文     
对用于临床数据预测的多个模型进行比较的可视分析系统
Yiran Li, Takanori Fujiwara, Yong K. Choi, Katherine K. Kim, Kwan-Liu Ma
University of California, Davis, United States
A visual analytics system for multi-model comparison on clinical data predictions
Yiran Li, Takanori Fujiwara, Yong K. Choi, Katherine K. Kim, Kwan-Liu Ma
University of California, Davis, United States
 全文: PDF 
摘要： 一个正在增长的趋势是将机器学习方法应用于医疗数据集来预测患者的未来状态。尽管其中一些方法取得了很好的效果，但如何通过它们的可解读信息来比较和评估不同的模型仍然存在挑战。进行这种分析有助于临床医生改善基于证据的医学诊断。 本文开发了一个可视分析系统，用来对多个模型的预测标准进行比较并评估它们的一致性。通过该系统，用户可以领会不同模型的内部标准以及每个模型对特定患者预测结果的可依赖性。通过对一个公开的临床数据集的案例研究，证明了本可视分析系统可有效地帮助临床医生和研究人员对不同机器学习方法进行比较和定量评估。
关键词： 临床数据;  XAI;  基于树;  机器学习模型;  模型一致性;  依赖性测量;  可视分析    
Abstract: 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.
Key words: Clinical data    XAI    Tree-based machine learning models    Model consistency    Measures of dependence    Visual analytics
出版日期: 2020-06-03
通讯作者: Yiran Li     E-mail: ranli@ucdavis.edu
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Yiran Li
Takanori Fujiwara
Yong K. Choi
Katherine K. Kim
Kwan-Liu Ma

引用本文:

Yiran Li, Takanori Fujiwara, Yong K. Choi, Katherine K. Kim, Kwan-Liu Ma. A visual analytics system for multi-model comparison on clinical data predictions. Vis Inf, 2020, 4(2): 122-131.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2020.04.005    或    http://www.zjujournals.com/vi/CN/Y2020/V4/I2/122

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