Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom evaluated quantitatively. This study compares the performance of different representations of clinical event data from MIMIC II in a logistic regression model to predict 36-hour ICU mortality. The most common representations are linear (normalized counts) and binary (yes/no). These, along with a new representation termed "hill", are compared using both L1 and L2 regularization. Results indicate that the introduced "hill" representation outperforms both the binary and linear representations, the hill representation thus has the potential to improve existing models of ICU mortality.

翻译：脑力综合症死亡率的良好预测者有可能更早地发现高风险患者,改进脑力综合症综合症资源分配,或创建更准确的人口风险模型。机器学习实践者通常会选择如何在特定模型中体现特征,但很少对这些选择进行定量评估。本研究比较了二期综合症综合症二期临床事件数据的不同表现表现,以预测36小时的重症综合症综合症死亡率。最常见的表现是线性(正常计数)和二元性(是/否 ) 。这些以及称为“丘”的新表现都使用L1和L2正规化方法进行比较。结果显示,引入的“丘”表现方式超越了二进制和线性表现方式,因此山地代表方式有可能改进现有的综合症综合症死亡率模式。