目的  基于机器学习算法构建血液透析患者发生中重度瘙痒的风险预测模型，筛选并验证最优模型。 方法  以2020年1月—2025年3月在南阳市中心医院血液净化中心行维持性血液透析的患者为研究对象。应用Logistic回归(logistic regression，LR)、随机森林(random forest，RF)、轻量级梯度提升机(lightweight gradient boosting machine，LightGBM)、极端梯度提升(extreme gradient boosting，XGBoost)、支持向量机(support vector machine，SVM)5种机器学习算法建立预测模型，比较受试者工作特征曲线下面积(area under curve，AUC)、敏感度、特异度、准确度、校准曲线和决策曲线评估模型性能。 结果  共纳入510例患者。纳入血清全段甲状旁腺激素(intact parathyroid hormone，iPTH)、血磷、皮肤干燥、睡眠质量评分、焦虑评分、抑郁评分6个因素构建风险预测模型。XGBoost预测模型在建模组与验证集中的AUC最高，分别为0.894(95%CI：0.828～0.961)、0.887(95%CI：0.801～0.973)；准确度分别为0.837、0.823，灵敏度分别为0.508、0.492，特异度分别为0.951、0.945，F1分数分别为0.614、0.591；校准曲线与理想曲线接近，决策曲线分析(decision curve analysis，DCA)曲线显示模型在8%～78%阈概率范围内净收益较高，具有较高的临床适用性。 结论  XGBoost模型可较好地预测血液透析患者发生中重度瘙痒的风险，有望为临床早期预警和个体化干预提供依据。

Objective  To construct a risk prediction model for moderate to severe pruritus in hemodialysis patients based on machine learning algorithms, and to screen and verify the optimal model.  Methods  A total of 510 patients who underwent maintenance hemodialysis in the blood purification center of our hospital from January 2020 to June 2025 were studied. Logistic regression (LR), random forest (RF), lightweight gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and support vector machine (SVM) were used to establish the prediction models. The performance of the models was evaluated by comparing the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, calibration curve and decision curve.  Results  Finally, six factors including intact parathyroid hormone (iPTH), blood phosphorus, dry skin, sleep quality score, anxiety score and depression score were included to construct a risk prediction model. The XGBoost prediction model had the highest AUC in the modeling group and the validation set, which were 0.894 (95% CI: 0.828～0.961) and 0.887 (95% CI: 0.801～0.973), respectively; the accuracies were 0.837 and 0.823, respectively, the sensitivities were 0.508 and 0.492, respectively, the specificities were 0.951 and 0.945, respectively, and the F1 scores were 0.614 and 0.591, respectively. The calibration curve was close to the ideal curve, and the decision curve analysis (DCA) showed that the model had a high net benefit in 8%～78% threshold probability range, indicating high clinical applicability.  Conclusion  The XGBoost model can effectively predict the risk of moderate to severe pruritus in hemodialysis patients, which is expected to provide a basis for early clinical warning and individualized intervention.