用机器学习提前预测脓毒症患者肾损伤，准确率超80%
Utilizing Machine Learning Models to Predict Acute Kidney Injury in Septic Patients from MIMIC-III Database
- 基于MIMIC-III数据构建逻辑回归模型，筛选23个关键临床指标
- 模型AUC达0.887，较现有最优结果提升8.57%，仅用13个更少变量
- 尿量、胆红素、肌酐等指标为关键预测因子，适合重症监护场景
脓毒症是严重感染引发的异常全身反应，常导致器官衰竭，其中急性肾损伤（AKI）发生率约50%，死亡率超40%。本文利用重症监护室（ICU）入院期间来自MIMIC-III数据库的脓毒症患者数据，共提取3301例，其中73%发展为AKI。数据随机划分为训练集（n=1980，40%）、测试集（n=661，10%）和验证集（n=660，50%）。采用逻辑回归模型，并与XGBoost、KNN、SVM、随机森林（RF）、LightGBM五种基线模型对比。评估指标包括AUC、准确率、F1分数、召回率和Brier得分。最终选定23个特征，前六位为尿量、最高胆红素、最低胆红素、体重、最高血尿素氮、最低估算肾小球滤过率。逻辑回归模型表现最佳，AUC为0.887（95% CI: [0.861–0.915]），准确率0.817，F1分数0.866，召回率0.827，Brier得分为0.13。相比该领域现有最优文献，本模型在AUC上提升8.57%，且使用变量减少13个，证明其在预测脓毒症相关肾损伤中的有效性。尽管部分特征与以往研究相似，但影响模型性能的关键特征存在差异。
原文摘要 · Abstract (English)
Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a major one being acute kidney injury (AKI). For septic patients, approximately 50% develop AKI, with a mortality rate above 40%. Creating models that can accurately predict AKI based on specific qualities of septic patients is crucial for early detection and intervention. Using medical data from septic patients during intensive care unit (ICU) admission from the Medical Information Mart for Intensive Care 3 (MIMIC-III) database, we extracted 3301 patients with sepsis, with 73% of patients developing AKI. The data was randomly divided into a training set (n = 1980, 40%), a test set (n = 661, 10%), and a validation set (n = 660, 50%). The proposed model was logistic regression, and it was compared against five baseline models: XGBoost, K Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and LightGBM. Area Under the Curve (AUC), Accuracy, F1-Score, and Recall were calculated for each model. After analysis, we were able to select 23 features to include in our model, the top features being urine output, maximum bilirubin, minimum bilirubin, weight, maximum blood urea nitrogen, and minimum estimated glomerular filtration rate. The logistic regression model performed the best, achieving an AUC score of 0.887 (95% CI: [0.861-0.915]), an accuracy of 0.817, an F1 score of 0.866, a recall score of 0.827, and a Brier score of 0.13. Compared to the best existing literature in this field, our model achieved an 8.57% improvement in AUC while using 13 fewer variables, showcasing its effectiveness in determining AKI in septic patients. While the features selected for predicting AKI in septic patients are similar to previous literature, the top features that influenced our model's performance differ.
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