Early Prediction of Sepsis via SMOTE Upsampling and Mutual Information Based Downsampling
Early Prediction of Sepsis via SMOTE Upsampling and Mutual Information Based Downsampling
复制标题
通过 SMOTE 上采样和基于互信息的下采样对脓毒症进行早期预测
DOI：
10.23919/cinc49843.2019.9005890
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
M. Motani
中科院分区：
文献类型：
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作者：
Shiyu Liu;Ming Lun Ong;K. Mun;Jia Yao;M. Motani
败血症是对感染的一种危及生命的反应，可导致组织损伤、器官衰竭和死亡。脓毒症的早期预测很重要，因为它减少了与晚期脓毒症休克相关的不良患者结果。然而，有效的早期预测是具有挑战性的，因为数据通常与阳性败血症诊断严重不平衡。如果不解决类不平衡问题，训练的模型将倾向于过度拟合，从而导致少数类的性能下降。本文提出了一种基于互信息的下采样算法和合成少数派过采样技术（SMOTE）的两步法，以有效地进行脓毒症的早期预测。我们的团队Kent Ridge AI（排名第77位）使用所提出的两步法在整个测试集上获得了-0.164的效用分数。此外，我们报告了交叉验证结果，并确定了几种提高性能的方法。
Sepsis is a life-threatening response to infection that can lead to tissue damage, organ failure and death. The early prediction of sepsis is important, as it reduces undesirable patient outcomes associated with late-stage septic shock. However, effective early prediction is challenging, because the data is often heavily imbalanced against positive sepsis diagnosis. If the class imbalance is not addressed, models trained will tend to overfit in favour of the majority class, leading to degraded performance on the minority class. In this paper, we suggest a two-step method which consists of a mutual information based downsampling algorithm and a Synthetic Minority Over-sampling Technique (SMOTE), in order to effectively perform early prediction of sepsis. Our team, Kent Ridge AI (ranked 77th), obtained a utility score of -0.164 on the full test set by using the proposed two-step method. Additionally, we report crossvalidation results and identify several methods to improve performance.
影响因子：
8.8
作者：
M. Reyna;C. Josef;S. Seyedi;R. Jeter;S. Shashikumar;M. Westover;Ashish Sharma;S. Nemati;
通讯作者：
M. Reyna;C. Josef;S. Seyedi;R. Jeter;S. Shashikumar;M. Westover;Ashish Sharma;S. Nemati;