Zhai Yihai, Lan Danxiu, Lv Siying, Mo Liqin
Cardiothoracic Surgery Intensive Care Unit, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Front Med (Lausanne). 2024 Jun 12;11:1399527. doi: 10.3389/fmed.2024.1399527. eCollection 2024.
The objective of this research was to create a machine learning predictive model that could be easily interpreted in order to precisely determine the risk of premature death in patients receiving intensive care after pulmonary inflammation.
In this study, information from the China intensive care units (ICU) Open Source database was used to examine data from 2790 patients who had infections between January 2019 and December 2020. A 7:3 ratio was used to randomly assign the whole patient population to training and validation groups. This study used six machine learning techniques: logistic regression, random forest, gradient boosting tree, extreme gradient boosting tree (XGBoost), multilayer perceptron, and K-nearest neighbor. A cross-validation grid search method was used to search the parameters in each model. Eight metrics were used to assess the models' performance: accuracy, precision, recall, F1 score, area under the curve (AUC) value, Brier score, Jordon's index, and calibration slope. The machine methods were ranked based on how well they performed in each of these metrics. The best-performing models were selected for interpretation using both the Shapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME) interpretable techniques.
A subset of the study cohort's patients (120/1668, or 7.19%) died in the hospital following screening for inclusion and exclusion criteria. Using a cross-validated grid search to evaluate the six machine learning techniques, XGBoost showed good discriminative ability, achieving an accuracy score of 0.889 (0.874-0.904), precision score of 0.871 (0.849-0.893), recall score of 0.913 (0.890-0.936), F1 score of 0.891 (0.876-0.906), and AUC of 0.956 (0.939-0.973). Additionally, XGBoost exhibited excellent performance with a Brier score of 0.050, Jordon index of 0.947, and calibration slope of 1.074. It was also possible to create an interactive internet page using the XGBoost model.
By identifying patients at higher risk of early mortality, machine learning-based mortality risk prediction models have the potential to significantly improve patient care by directing clinical decision making and enabling early detection of survival and mortality issues in patients with pulmonary inflammation disease.
本研究的目的是创建一个易于解释的机器学习预测模型，以精确确定肺部炎症后接受重症监护患者的过早死亡风险。
在本研究中，使用来自中国重症监护病房（ICU）开源数据库的信息，对2019年1月至2020年12月期间2790例感染患者的数据进行分析。采用7:3的比例将全部患者随机分为训练组和验证组。本研究使用了六种机器学习技术：逻辑回归、随机森林、梯度提升树、极端梯度提升树（XGBoost）、多层感知器和K近邻算法。采用交叉验证网格搜索方法对每个模型的参数进行搜索。使用八个指标评估模型的性能：准确率、精确率、召回率、F1分数、曲线下面积（AUC）值、布里尔分数、乔丹指数和校准斜率。根据这些指标中各模型的表现对机器方法进行排名。使用夏普利值加法解释（SHAP）和局部可解释模型无关解释（LIME）可解释技术，选择性能最佳的模型进行解释。
在纳入和排除标准筛选后，研究队列中的一部分患者（120/1668，即7.19%）在医院死亡。使用交叉验证网格搜索评估六种机器学习技术，XGBoost显示出良好的判别能力，准确率得分为0.889（0.874 - 0.904），精确率得分为0.871（0.849 - 0.893），召回率得分为0.913（0.890 - 0.936），F1分数为0.891（0.876 - 0.906），AUC为0.956（0.939 - 0.973）。此外，XGBoost表现出色，布里尔分数为0.050，乔丹指数为0.947，校准斜率为1.074。还可以使用XGBoost模型创建一个交互式网页。
通过识别早期死亡风险较高的患者，基于机器学习的死亡风险预测模型有可能通过指导临床决策和早期发现肺部炎症疾病患者的生存和死亡问题，显著改善患者护理。
