Studying the Potential of Multi-target Classification to Characterize Combinations of Classes with Skewed Distribution

Arne Schneck, S. Kalle, R. Pryss, W. Schlee, T. Probst, B. Langguth, M. Landgrebe, M. Reichert, M. Spiliopoulou
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引用次数: 4

Abstract

The identification of subpopulations with particular characteristics with respect to a disease is important for personalized diagnostics and therapy design. For some diseases, the outcome is described by more than one target variable. An example is tinnitus: the perceived loudness of the phantom signal and the level of distress caused by it are both relevant targets for diagnosis and therapy. In this work, we study the potential of multi-target classification for the identification of those screening variables, which separate best among the different subpopulations of patients, paying particular attention to subpopulations with discordant value combinations of loudness and distress. We analyse the screening data of 1344 tinnitus patients from the University Hospital Regensburg, including questions from 7 questionnaires, and report on the performance of our workflow in target separation and in ranking the questionnaires variables on their discriminative power.
研究多目标分类对偏斜分布类组合特征的潜力
识别具有特定疾病特征的亚群对于个性化诊断和治疗设计非常重要。对于某些疾病,结果由多个目标变量描述。耳鸣就是一个例子:感知到的幻象信号的响度和由此引起的痛苦程度都是诊断和治疗的相关目标。在这项工作中,我们研究了多目标分类识别这些筛选变量的潜力,这些筛选变量在不同的患者亚群中分离得最好,特别关注响度和痛苦值组合不一致的亚群。我们分析了来自雷根斯堡大学医院的1344名耳鸣患者的筛查数据,包括7份问卷的问题,并报告了我们的工作流程在目标分离和问卷变量判别能力排序方面的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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