预测 ICU 2 型糖尿病患者呼吸机相关性肺炎的数据预处理、基线特征、变量相关分析和模型评估、TRIPODAI Guide

Name: 预测 ICU 2 型糖尿病患者呼吸机相关性肺炎的数据预处理、基线特征、变量相关分析和模型评估、TRIPODAI Guide
Creator: Aoxing, Shi
Published: 2025-10-29 00:00:00
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Figshare2025-10-29 更新2026-04-08 收录

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https://figshare.com/articles/dataset/Data_Preprocessing_Baseline_Characteristics_Variable_Correlation_Analysis_and_Model_Evaluation_for_Predicting_Ventilator-Associated_Pneumonia_in_ICU_Patients_with_Type_2_Diabetes_Mellitus/30454706/2

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该数据集包含一项针对 ICU 2 型糖尿病患者的研究的基线特征和补充数据，旨在使用机器学习预测呼吸机相关性肺炎（VAP）。基线特征表总结了患者人口统计学、生命体征和实验室值。补充图说明了数据预处理步骤（四分位距清理前后的直方图和箱线图）、使用随机森林的缺失值插补、变量相关分析（Spearman 热图）和模型评估（四个预测模型的混淆矩阵）。这些数据提供了特征选择、清理程序和模型性能评估的详细概述。Fig. S1. Histograms and boxplots of Glucose_max and SBP_max in original and cleaned datasets: Glucose_max, maximum blood glucose; SBP_max, maximum systolic blood pressure. (A) original Glucose_max; (B) cleaned Glucose_max; (C) original SBP_max; (D) cleaned SBP_max. Fig. S2. Histograms and boxplots of Temp_min and WBC_min in original and cleaned datasets: Temp_min, minimum body temperature; WBC_min, minimum white blood cell count.(A)original Temp_min; (B)cleaned Temp_min; (C)original WBC_min; (D)cleaned WBC_min.Fig. S3. Histograms of PH_max and PH_min in original and Random Forest–imputed datasets: PH_max, maximum pH; PH_min, minimum pH.Fig. S4. Histograms of PO2_max and PO2_min in original and Random Forest–imputed datasets: PO2_max, maximum partial pressure of oxygen; PO2_min, minimum partial pressure of oxygen.Fig. S5. Histograms of PT_max and PT_min in original and Random Forest–imputed datasets: PT_max, maximum prothrombin time; PT_min, minimum prothrombin time.Fig. S6. Spearman correlation heatmap of variables selected by both the Boruta algorithm and LASSO regression:Hypertension, history of hypertension; Temp_min, minimum body temperature; Glusco_max, maximum blood glucose; Scr_max, maximum serum creatinine; WBC_min, minimum white blood cell count;CNS, SOFA neurological subscore; Renal, SOFA renal subscore; and GCS, Glasgow Coma Scale.Fig. S7. Confusion matrices of four predictive models: (A) Logistic Regression, (B) Random Forest, (C) XGBoost, and (D) Gradient Boosting Machine (GBM). Each matrix presents the counts of true positives, true negatives, false positives, and false negatives, facilitating model performance comparison.

提供机构：

Aoxing, Shi

创建时间：

2025-10-29