Zhou Yiwei, Wu Jian, Xu Xin, Shi Guirong, Liu Ping, Jiang Liping
Business School, University of Shanghai for Science and Technology, Shanghai, China.
School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai, China.
Biosci Trends. 2025 May 9;19(2):173-188. doi: 10.5582/bst.2025.01013. Epub 2025 Apr 15.
This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healthcare problem, often leading to prolonged hospitalization and increased medical costs. Conventional risk assessment scales are limited in their ability to predict PIs accurately, prompting the exploration of ML techniques to address this challenge.We utilized data from 7,292 patients admitted to a tertiary care hospital in Shanghai between May 2017 and July 2023, with a final dataset of 2,972 patients, including 158 with PIs. Seven ML algorithms-Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Extra Trees (ET), K-Nearest Neighbors (KNN), and Decision Trees (DT)-were used in conjunction with SMOTE, SMOTE+ENN, Borderline-SMOTE, ADASYN, and GAN to balance the dataset and improve model performance.Results revealed significant improvements in model performance when SMOTE and its variants were used. For instance, the XGBoost model hadan AUC of 0.996 with SMOTE, compared to 0.800 on raw data. SMOTE+ENN and Borderline-SMOTE further enhanced the models' ability to identify minority classes. External validation indicatedthat XGBoost, RF, and ET exhibited the highest stability and accuracy, with XGBoost having an AUC of 0.977. SHAP analysis revealed that factors such as anesthesia grade, age, and serum albumin levels significantly influenced model predictions.In conclusion, integrating SMOTE with ML algorithms effectively addressed a data imbalance and improved the prediction of perioperative PIs. Future work should focus on refining SMOTE techniques and exploring their application to larger, multi-center datasets to enhance the generalizability of these findings, and especially for diseaseswith a lowincidence.
本研究调查了将机器学习（ML）模型与合成少数过采样技术（SMOTE）及其变体相结合，以预测不平衡数据集中围手术期压力性损伤（PI）的情况。压力性损伤是一个重大的医疗保健问题，常常导致住院时间延长和医疗费用增加。传统的风险评估量表在准确预测压力性损伤方面能力有限，这促使人们探索机器学习技术来应对这一挑战。
我们利用了2017年5月至2023年7月期间在上海一家三级护理医院收治的7292例患者的数据，最终数据集为2972例患者，其中包括158例发生压力性损伤的患者。七种机器学习算法——支持向量机（SVM）、逻辑回归（LR）、随机森林（RF）、极端梯度提升（XGBoost）、额外树（ET）、K近邻（KNN）和决策树（DT）——与SMOTE、SMOTE + ENN、边界SMOTE、ADASYN和生成对抗网络（GAN）结合使用，以平衡数据集并提高模型性能。
结果显示，使用SMOTE及其变体时模型性能有显著提高。例如，XGBoost模型在使用SMOTE时的曲线下面积（AUC）为0.996，而原始数据的AUC为0.800。SMOTE + ENN和边界SMOTE进一步增强了模型识别少数类别的能力。外部验证表明，XGBoost、RF和ET表现出最高的稳定性和准确性，XGBoost的AUC为0.977。SHAP分析表明，麻醉分级、年龄和血清白蛋白水平等因素对模型预测有显著影响。
总之，将SMOTE与机器学习算法相结合有效地解决了数据不平衡问题，并改善了围手术期压力性损伤的预测。未来的工作应侧重于改进SMOTE技术，并探索其在更大的多中心数据集上的应用，以提高这些发现的普遍性，特别是对于低发病率的疾病。
