Discovery and inclusion of SOFA score episodes in mortality prediction
Discovery and inclusion of SOFA score episodes in mortality prediction
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DOI：
10.1016/j.jbi.2007.03.007
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发表时间：
2007-12-01
影响因子：
4.5
通讯作者：
Bosman, Robert-Jan
中科院分区：
文献类型：
--
作者：
Toma, Tudor;Abu-Hanna, Ameen;Bosman, Robert-Jan
预测重症监护患者在住院结束时的生存状态对于各种临床和组织任务是有用的。目前用于预测死亡率的模型使用逻辑回归模型，其仅依赖于患者入院的前24小时期间收集的数据。这些模型不利用包含在每日器官衰竭评分中的信息，这些评分现在在许多重症监护室中常规收集。我们提出了一种新的死亡率预测方法，除了入院相关的数据，还利用了日常数据。该方法的特点是由数据驱动的发现时间模式，称为情节，器官衰竭评分，并将它们嵌入到熟悉的逻辑回归框架进行预测。我们的方法导致了一组D逻辑回归模型，每个D天的重症监护病房停留。D日的模特
Predicting the survival status of Intensive Care patients at the end of their hospital stay is useful for various clinical and organizational tasks. Current models for predicting mortality use logistic regression models that rely solely on data collected during the first 24 h of patient admission. These models do not exploit information contained in daily organ failure scores which nowadays are being routinely collected in many Intensive Care Units. We propose a novel method for mortality prediction that, in addition to admission-related data, takes advantage of daily data as well. The method is characterized by the data-driven discovery of temporal patterns, called episodes, of the organ failure scores and by embedding them in the familiar logistic regression framework for prediction. Our method results in a set of D logistic regression models, one for each of the first D days of Intensive Care Unit stay. A model for day d