Verduijn Marion, Dagliati Arianna, Sacchi Lucia, Peek Niels, Bellazzi Riccardo, de Jonge Evert, de Mol Bas
Dept. of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands. m,
AMIA Annu Symp Proc. 2005;2005:749-53.
This paper presents an empirical comparison of two temporal abstraction procedures, that were applied to derive predictive features for a prediction problem in intensive care medicine. The first procedure employs knowledge from practitioners to derive qualitative patterns of state changes; the second procedure searches through a large number of data summaries to discover those that have predictive value. The derived features were used to predict whether postsurgical patients would need mechanical ventilation longer then 24h. The data-driven temporal abstraction procedure was found to provide more informative predictors, resulting in better predictions.
本文对两种时间抽象过程进行了实证比较，这两种过程被应用于为重症监护医学中的一个预测问题推导预测特征。第一种过程利用从业者的知识来推导状态变化的定性模式；第二种过程在大量数据摘要中进行搜索，以发现具有预测价值的数据摘要。所推导的特征被用于预测术后患者是否需要机械通气超过24小时。结果发现，数据驱动的时间抽象过程能提供更多信息丰富的预测指标，从而带来更好的预测效果。
