基于机器学习技术的老年急性胆囊炎患者PTGBD术后行LC手术时机预测模型的建立  

Establishment of a Prediction Model for Determining the Timing of LC after PTGBD in Elderly Patients with Acute Cholecystitis Based on Machine Learning Technology

作　　者：张维 杨楠 张卫彬[3] 

机构地区：[1]锦州医科大学研究生培养基地,辽宁 锦州 [2]襄阳市妇幼保健院外科,湖北 襄阳 [3]锦州市中心医院普外科,辽宁 锦州

出　　处：《临床医学进展》2025年第3期2310-2324,共15页Advances in Clinical Medicine

摘　　要：回顾性分析2013年1月至2024年6月在锦州市中心医院收治的PTGBD术后行LC的老年急性胆囊炎患者的临床资料。采用Logistic回归(LR)、支持向量机(SVM)、决策树(DT)、随机森林(RF)和人工神经网络算法(ANN)对得到的数据集进行预测模型的构建。使用交叉验证进行内部验证并采用曲线下面积和Brier评分测量值对模型的分化程度和校准度进行评价和比较。最终确定了6个危险因素,包括:年龄、体温、白细胞、胆囊壁厚度、碱性磷酸酶及血尿素氮,并在此基础上建立预测模型,通过比较不同模型的敏感性、特异性、阳性预测值、阴性预测值、临床决策曲线(DCA)和校准曲线,5个模型均显示出良好的预测性能和稳定性,可被视为临床决策的辅助手段。The clinical data of elderly patients with acute cholecystitis who underwent LC after PTGBD and were admitted to the Central Hospital of Jinzhou from January 2013 to June 2024 were retrospectively analyzed. Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) were used to construct the prediction model of the obtained dataset. Internal validation was performed using cross-validation, and the degree of differentiation and calibration of the models were evaluated and compared using the area under the curve and Brier score measurements. Finally, six risk factors were identified, including age, body temperature, white blood cells, gallbladder wall thickness, alkaline phosphatase, and blood urea nitrogen, and a prediction model was established on this basis and by comparing the sensitivity, specificity, positive predictive value, negative predictive value, clinical decision curve (DCA) and calibration curve of different models, the five models showed good predictive performance and stability, which can be regarded as an auxiliary means for clinical decision-making.

关 键 词：急性胆囊炎 经皮经肝胆囊穿刺引流术 腹腔镜下胆囊切除术 机器学习 危险因素 预测模型 

分 类 号：R657.4[医药卫生—外科学]