Automatic categorization of health indices for risk quantification
Automatic categorization of health indices for risk quantification
复制标题
健康指数自动分类以进行风险量化
DOI：
10.1016/j.procs.2015.08.350
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
S.
中科院分区：
文献类型：
--
作者：
Kanemura;A.;Lipowski;G.;Komine;H.;and Akaho;S.
将健康数据分类通常用于分析和了解健康风险；然而，类别分界点的选择并不是一项简单的任务，错误可能会导致对数据的错误解释。由于分界点选择不当可能会导致不可靠和错误的结论，因此需要一种自动方法来平衡构建类别的偏差和方差，并允许验证可用数据量是否足以得出结论。这种方法对于在实验计划中决定下一步行动也很有用。我们在此表明，涉及广泛可比性的健康数据需要更好的截止点估计公式，并演示如何将用于比较分类的不同方法应用于此类数据。我们的方法可以帮助实现数据分析流程的自动化，并促进健康数据的科学发现。
Classification of health data into categories is routinely used for the analysis and understanding of health risks; however, the selection of cut-off points of categories is not a simple task, and mistakes can lead to incorrect interpretation of data. Since inappropriate selection of the cut-off points can lead to unreliable and wrong conclusions, it is desirable to have an automatic method that balances the bias and the variance for constructing categories, and which allows the verification if the amount of available data is enough to draw a conclusion. Such a method is also useful in making decisions on next actions in experiment planning. We show here that a better formulation of cut-off point estimation is required for health data involving wide comparability, and demonstrate how a different method for comparing categorizations can be applied to such data. Our method can help in automation of data analysis pipeline and in promotion of scientific discoveries from health data.