School of Mathematics, Southeast University, Nanjing 211189, China.
Comput Intell Neurosci. 2021 Oct 12;2021:6522633. doi: 10.1155/2021/6522633. eCollection 2021.
Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.
脓毒症是一种器官衰竭疾病，由感染引起，死亡率极高。应用机器学习算法 XGBoost 和 LightGBM 构建两种处理方法：均值处理方法和特征生成方法，旨在提前 6 小时预测早期脓毒症。特征生成方法通过结合不同的特征（包括统计强度特征、窗口特征和医学特征）构建而成。应用 Miceforest 多重插值方法解决大规模缺失数据问题。结果表明，特征生成方法优于均值处理方法。XGBoost 和 LightGBM 算法在预测性能方面均表现出色（AUC：0.910∼0.979），其中 LightGBM 具有更快的运行速度，尤其在多维数据方面具有更强的泛化能力，在特征生成方法中 AUC 达到 0.979。PTT、WBC 和血小板是预测早期脓毒症的关键风险因素。
