构建使用了PD-1抑制剂的肿瘤患者出现甲状腺功能障碍的风险预测模型,分析使用PD-1肿瘤抑制剂导致的甲状腺功能障碍的相关风险因素,设计监测预警系统。选取2020年—2023年广西医科大学附属肿瘤医院1225例使用PD-1抑制剂肿瘤患者的临床资...构建使用了PD-1抑制剂的肿瘤患者出现甲状腺功能障碍的风险预测模型,分析使用PD-1肿瘤抑制剂导致的甲状腺功能障碍的相关风险因素,设计监测预警系统。选取2020年—2023年广西医科大学附属肿瘤医院1225例使用PD-1抑制剂肿瘤患者的临床资料,包括人口学特征、既往史、实验室检测等63个变量。本文选取相关性前10/20/30/40/50/60个变量的4种传统机器学习模型进行性能比较。通过F1分数、灵敏度、准确率、精确率、特异性曲线下面积(Area Under the Curve,AUC)评估以上预测模型的性能,并利用Shapley加性解释(Shapley Additive Explanation,SHAP)可视化解释本文的机器学习模型。与促甲状腺激素相关性排名前10的变量依次为:羟丁酸脱氢酶、乳酸脱氢酶、淋巴细胞绝对值、天门冬氨酸转移酶、钙离子、碱性磷酸酶、谷氨酰转肽酶、单核细胞绝对值、红细胞分布宽度SD、胆碱酯酶。建立了使用PD-1抑制剂的肿瘤患者出现甲状腺功能障碍的风险预测模型,并在全局解释和局部解释的层面上分别作出模型预测结果影响的解释。展开更多
Urban rainwater runoff is an important source of nonpoint source pollution due to its transport of diverse contaminants,including polycyclic aromatic hydrocarbons(PAHs)and chlorinated derivatives.Importantly,these chl...Urban rainwater runoff is an important source of nonpoint source pollution due to its transport of diverse contaminants,including polycyclic aromatic hydrocarbons(PAHs)and chlorinated derivatives.Importantly,these chlorinated polycyclic aromatic hydrocarbons(Cl-PAHs)exhibit elevated toxicological potential compared to their non-halogenated parent compounds.In this study,we proposed an approach that combined multivariate receptor model with integration of SHapley Additive exPlanations and Random Forest model.This method identifies the possible sources and reveals the impact of source apportionment results and environmental driving factors(such as geographical and meteorological data)on pollutant concentrations.Sixteen PAHs and nine ClPAHs were detected in 79 runoff samples from all three sites.TheΣ_(16)PAHs average concentration(2923.93 to 6071.83 ng/L)was significantly higher than theΣ_(9)Cl-PAHs(384.34 to 1314.73 ng/L).The source apportionment was conducted by positive matrix factorization(PMF),and six potential pollution sources for PAHs and three for Cl-PAHs were quantified.PAHs primarily originate from the combustion of fossil fuels such as traffic,industrial emissions and coal tar,while Cl-PAHs are mainly derived from atmospheric deposition and industrial emissions.Meanwhile,the self‑organizing map classified PAHs and Cl-PAHs into 2 and 3 groups,respectively.The k-means algorithm yielded 4 clusters for runoff samples.Among machine learning models,Random Forest(RF)demonstrated optimal predictive performance and integrated with SHapley Additive exPlanations(RF-SHAP)revealed the effects of driving factors on the predicted concentration of PAHs and Cl-PAHs in urban runoff samples.展开更多
文摘构建使用了PD-1抑制剂的肿瘤患者出现甲状腺功能障碍的风险预测模型,分析使用PD-1肿瘤抑制剂导致的甲状腺功能障碍的相关风险因素,设计监测预警系统。选取2020年—2023年广西医科大学附属肿瘤医院1225例使用PD-1抑制剂肿瘤患者的临床资料,包括人口学特征、既往史、实验室检测等63个变量。本文选取相关性前10/20/30/40/50/60个变量的4种传统机器学习模型进行性能比较。通过F1分数、灵敏度、准确率、精确率、特异性曲线下面积(Area Under the Curve,AUC)评估以上预测模型的性能,并利用Shapley加性解释(Shapley Additive Explanation,SHAP)可视化解释本文的机器学习模型。与促甲状腺激素相关性排名前10的变量依次为:羟丁酸脱氢酶、乳酸脱氢酶、淋巴细胞绝对值、天门冬氨酸转移酶、钙离子、碱性磷酸酶、谷氨酰转肽酶、单核细胞绝对值、红细胞分布宽度SD、胆碱酯酶。建立了使用PD-1抑制剂的肿瘤患者出现甲状腺功能障碍的风险预测模型,并在全局解释和局部解释的层面上分别作出模型预测结果影响的解释。
基金supported by Guangdong Basic and Applied Basic Research Foundation(Nos.2021B1515120055 and 2022A1515010499).
文摘Urban rainwater runoff is an important source of nonpoint source pollution due to its transport of diverse contaminants,including polycyclic aromatic hydrocarbons(PAHs)and chlorinated derivatives.Importantly,these chlorinated polycyclic aromatic hydrocarbons(Cl-PAHs)exhibit elevated toxicological potential compared to their non-halogenated parent compounds.In this study,we proposed an approach that combined multivariate receptor model with integration of SHapley Additive exPlanations and Random Forest model.This method identifies the possible sources and reveals the impact of source apportionment results and environmental driving factors(such as geographical and meteorological data)on pollutant concentrations.Sixteen PAHs and nine ClPAHs were detected in 79 runoff samples from all three sites.TheΣ_(16)PAHs average concentration(2923.93 to 6071.83 ng/L)was significantly higher than theΣ_(9)Cl-PAHs(384.34 to 1314.73 ng/L).The source apportionment was conducted by positive matrix factorization(PMF),and six potential pollution sources for PAHs and three for Cl-PAHs were quantified.PAHs primarily originate from the combustion of fossil fuels such as traffic,industrial emissions and coal tar,while Cl-PAHs are mainly derived from atmospheric deposition and industrial emissions.Meanwhile,the self‑organizing map classified PAHs and Cl-PAHs into 2 and 3 groups,respectively.The k-means algorithm yielded 4 clusters for runoff samples.Among machine learning models,Random Forest(RF)demonstrated optimal predictive performance and integrated with SHapley Additive exPlanations(RF-SHAP)revealed the effects of driving factors on the predicted concentration of PAHs and Cl-PAHs in urban runoff samples.
