基于机器学习的重症患者脓毒症实时风险预测模型

脓毒症是人体对感染反应失调导致的器官功能障碍综合症,具有较高的发病率和死亡率。传统的评分系统存在特异性低的问题。本研究基于LightGBM机器学习框架,提出了一种对脓毒症进行早期预测和风险评估的模型,以便对具有脓毒症潜在风险的患者进行及时干预。为了实现该模型,提出基于LASSO特征选择和滑动窗口路径重积分的时间序列特征构建方法,以及基于动态时间规整算法的时间序列聚类采样方法。选择MIMIC-Ⅲ数据库29239位病人和PhysioNet/CinC 2019挑战赛数据集40336位病人的临床信息来训练和验证模型。所提出的模型在MIMIC-Ⅲ和PhysioNet/CinC 2019独立测试集上的灵敏度、特异性、操作特征曲线下面积(AUC)分别为0.7377、0.7304、0.8147和0.8026、0.7891、0.8730。与目前最先进的EASP方法相比,AUC分别提高了3.62%和2.83%。本研究模型可以实时预测脓毒症发生的风险,揭示影响脓毒症发生的重要因素,为脓毒症风险人群的及时干预提供依据。

基于代谢组学策略的脓毒症研究进展

脓毒症是一种异质性疾病,其发病机制复杂、临床表现多样、患者群体具有异质性。脓毒症时机体的代谢稳态被破坏,代谢物的初始差异可能预示着疾病的严重程度,随着时间的推移代谢物发生的变化可能有助于评估治疗反应、预测疾病进展或临床结果。由于脓毒症的异质性,代谢组学的特征使其在脓毒症评估中发挥了作用,阐明了脓毒症病理生理学中最相关的代谢产物的水平,并强调了脓毒症中特定生化途径,且更加突出了脓毒症幸存者和非幸存者之间的生化差异。代谢组学在脓毒症的诊断、预后、危险分层以及药物靶点的识别方面已经取得了一些研究成果,使脓毒症精准医学和靶向治疗成为可能。

Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning

Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIMIC-Ⅳ database.Clinical features were generated and selected by mutual information and grid search.Logistic regression,Random forest,LightGBM,XGBoost,and other machine learning models were constructed to predict the mortality probability.Five measurements including accuracy,precision,recall,F1 score,and area under curve(AUC) were acquired for model evaluation.An external validation was implemented to avoid conclusion bias.Results LightGBM outperformed other methods,achieving the highest AUC(0.900),accuracy(0.808),and precision(0.559).All machine learning models performed better than SAPSⅡ score(AUC=0.748).LightGBM achieved 0.883 in AUC in the external data validation.Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS Ⅱ score.