Objective：To assess prognostic factors and validate the effectiveness of recursive partitioning analysis （RPA） classes and graded prognostic assessment （GPA） in 290 non-small cell lung cancer （NSCLC） patients w...Objective：To assess prognostic factors and validate the effectiveness of recursive partitioning analysis （RPA） classes and graded prognostic assessment （GPA） in 290 non-small cell lung cancer （NSCLC） patients with brain metastasis （BM）.Methods：From Jan 2008 to Dec 2009,the clinical data of 290 NSCLC cases with BM treated with multiple modalities including brain irradiation,systemic chemotherapy and tyrosine kinase inhibitors （TKIs） in two institutes were analyzed.Survival was estimated by Kaplan-Meier method.The differences of survival rates in subgroups were assayed using log-rank test.Multivariate Cox＇s regression method was used to analyze the impact of prognostic factors on survival.Two prognostic indexes models （RPA and GPA） were validated respectively.Results：All patients were followed up for 1-44 months,the median survival time after brain irradiation and its corresponding 95% confidence interval （95% CI） was 14 （12.3-15.8） months.1-,2-and 3-year survival rates in the whole group were 56.0%,28.3%,and 12.0%,respectively.The survival curves of subgroups,stratified by both RPA and GPA,were significantly different （P0.001）.In the multivariate analysis as RPA and GPA entered Cox＇s regression model,Karnofsky performance status （KPS） ≥ 70,adenocarcinoma subtype,longer administration of TKIs remained their prognostic significance,RPA classes and GPA also appeared in the prognostic model.Conclusion：KPS ≥70,adenocarcinoma subtype,longer treatment of molecular targeted drug,and RPA classes and GPA are the independent prognostic factors affecting the survival rates of NSCLC patients with BM.展开更多
Background：The role ofpostradiation systemic therapy in non-small cell lung cancer （NSCLC） patients with brain metastasis （BM） was controversial.Thus,we explored the role of Radiation Therapy Oncology Group recur...Background：The role ofpostradiation systemic therapy in non-small cell lung cancer （NSCLC） patients with brain metastasis （BM） was controversial.Thus,we explored the role of Radiation Therapy Oncology Group recursive partitioning analysis （RTOG-RPA） and graded prognostic assessment （GPA） in identifying population who may benefit from postradiation systemic therapy.Methods：The clinical data of NSCLC patients with documented BM from August 2007 to April 2015 of two hospitals were studied retrospectively.Cox regression was used for multivariate analysis.Survival of patients with or without postradiation systemic therapy was compared in subgroups stratified according to RTOG-RPA or GPA.Results：Of 216 included patients,67.1% received stereotactic radiosurgery （SRS）,24.1% received whole-brain radiation therapy （WBRT）,and 8.8% received both.After radiotherapy,systemic therapy was administered in 58.3% of patients.Multivariate analysis found that postradiation systemic therapy （yes vs.no） （hazard ratio [HR] =0.36 l,95% confidence interval [CI] =0.202-0.648,P =0.001）,radiation technique （SRS vs.WBRT） （HR =0.462,95% CI =0.238-0.849,P =0.022）,extracranial metastasis （yes vs.no） （HR =3.970,95% CI =1.757-8.970,P =0.001）,and Kamofsky performance status （〈70 vs.≥70） （HR =5.338,95% CI =2.829-10.072,P 〈 0.001） were independent factors for survival.Further analysis found that subsequent tyrosine kinase inhibitor （TKI） therapy could significantly reduce the risk of mortality of patients in RTOG-RPA Class IⅡ （HR =0.411,95% CI =0.183-）.923,P =0.031） or with a GPA score of 1.5-2.5 （HR =0.420,95% CI =0.182-0.968,P =0.042）.However,none of the subgroups stratified according to RTOG-RPA or GPA benefited from the additional conventional chemotherapy.Conclusion：RTOG-RPA and GPA may be useful to identify beneficial populations in NSCLC patients with BM ifTKIs were chosen as postradiation systemic therapy.展开更多
Numerous previous literature has attempted to apply machine learning techniques to analyze relationships between energy variables in energy consumption.However,most machine learning methods are primarily used for pred...Numerous previous literature has attempted to apply machine learning techniques to analyze relationships between energy variables in energy consumption.However,most machine learning methods are primarily used for prediction through complicated learning processes at the expense of interpretability.Those methods have difficulties in evaluating the effect of energy variables on energy consumption and especially capturing their heterogeneous relationship.Therefore,to identify the energy consumption of the heterogeneous relationships in actual buildings,this study applies the MOdel-Based recursive partitioning(MOB)algorithm to the 2012 CBECS survey data,which would offer representative information about actual commercial building characteristics and energy consumption.With resultant tree-structured subgroups,the MOB tree reveals the heterogeneous effect of energy variables and mutual influences on building energy consumptions.The results of this study would provide insights for architects and engineers to develop energy conservative design and retrofit in U.S.office buildings.展开更多
Interior Alaska has a short growing season of 110 d.The knowledge of timings of crop flowering and maturity will provide the information for the agricultural decision making.In this study,six machine learning algorith...Interior Alaska has a short growing season of 110 d.The knowledge of timings of crop flowering and maturity will provide the information for the agricultural decision making.In this study,six machine learning algorithms,namely Linear Discriminant Analysis(LDA),Support Vector Machines(SVMs),k-nearest neighbor(kNN),Naïve Bayes(NB),Recursive Partitioning and Regression Trees(RPART),and Random Forest(RF),were selected to forecast the timings of barley flowering and maturity based on the Alaska Crop Datasets and climate data from 1991 to 2016 in Fairbanks,Alaska.Among 32 models fit to forecast flowering time,two from LDA,12 from SVMs,four from NB,three from RF outperformed models from other algorithms with the highest accuracy.Models from kNN performed worst to forecast flowering time.Among 32 models fit to forecast maturity time,two models from LDA outperformed the models from other algorithms.Models from kNN and RPART performed worst to forecast maturity time.Models from machine learning methods also provided a variable importance explanation.In this study,four out of six algorithms gave the same variable importance order.Sowing date was the most important variable to forecast flowering but less important variable to forecast maturity.The daily maximum temperature may be more important than daily minimum temperature to fit flowering models while daily minimum temperature may be more important than daily maximum temperature to fit maturity models.The results indicate that models from machine learning provide a promising technique in forecasting the timings of flowering and maturity of barley.展开更多
Background: The vasoactive-ventilation-renal (VVR) score includes pulmonary and renal dysfunctions not previously addressed by the vasoactive inotrope score (VIS) and may be a better predictor of cardiac care unit (CC...Background: The vasoactive-ventilation-renal (VVR) score includes pulmonary and renal dysfunctions not previously addressed by the vasoactive inotrope score (VIS) and may be a better predictor of cardiac care unit (CCU) length of stay (LOS) in patients undergoing re-entry sternotomy (defined as no earlier than 30 days after previous sternotomy) for congenital heart disease (CHD). Methods: Patients undergoing re-entry sternotomy for CHD from August 1, 2009 to June 30, 2016 were studied retrospectively. A total of 96 patients undergoing 133 re-entry procedures were identified. VVR scores were calculated on CCU admission post-procedure (at 0 hour), 24-hour, and 48-hour after admission to the CCU. The response variable was CCU LOS.? Recursive partition analysis identified variables predicting LOS. Results: 133 re-entry sternotomies in 96 patients made up the samples of the database;11 samples were removed due to incomplete data or placement on ECMO. Of the initial 25 features, 5 were removed for near zero variance and 3 categorical features were removed for non-information. Covariance analysis did not demonstrate any significant correlation amongst the remaining features. Initial recursive tree regression using ANOVA, cross validation and conditional predictive p-value (cp) = 0.01 produced 3 trees. The tree with lowest cross validation error was selected. The resulting 2 split trees with ventilator days less than 20 days and VVR score at 48 hours greater than 23 identified three CCU LOS groups with mean CCU LOS of 77.6, 55.1, and 9.5 days. Conclusions: Recursive partition analysis identified ventilator days greater than 20 days and the sub-population VVR at 48 hours as predictive of CCU LOS in patients undergoing re-entry sternotomy for CHD.展开更多
文摘Objective：To assess prognostic factors and validate the effectiveness of recursive partitioning analysis （RPA） classes and graded prognostic assessment （GPA） in 290 non-small cell lung cancer （NSCLC） patients with brain metastasis （BM）.Methods：From Jan 2008 to Dec 2009,the clinical data of 290 NSCLC cases with BM treated with multiple modalities including brain irradiation,systemic chemotherapy and tyrosine kinase inhibitors （TKIs） in two institutes were analyzed.Survival was estimated by Kaplan-Meier method.The differences of survival rates in subgroups were assayed using log-rank test.Multivariate Cox＇s regression method was used to analyze the impact of prognostic factors on survival.Two prognostic indexes models （RPA and GPA） were validated respectively.Results：All patients were followed up for 1-44 months,the median survival time after brain irradiation and its corresponding 95% confidence interval （95% CI） was 14 （12.3-15.8） months.1-,2-and 3-year survival rates in the whole group were 56.0%,28.3%,and 12.0%,respectively.The survival curves of subgroups,stratified by both RPA and GPA,were significantly different （P0.001）.In the multivariate analysis as RPA and GPA entered Cox＇s regression model,Karnofsky performance status （KPS） ≥ 70,adenocarcinoma subtype,longer administration of TKIs remained their prognostic significance,RPA classes and GPA also appeared in the prognostic model.Conclusion：KPS ≥70,adenocarcinoma subtype,longer treatment of molecular targeted drug,and RPA classes and GPA are the independent prognostic factors affecting the survival rates of NSCLC patients with BM.
文摘Background：The role ofpostradiation systemic therapy in non-small cell lung cancer （NSCLC） patients with brain metastasis （BM） was controversial.Thus,we explored the role of Radiation Therapy Oncology Group recursive partitioning analysis （RTOG-RPA） and graded prognostic assessment （GPA） in identifying population who may benefit from postradiation systemic therapy.Methods：The clinical data of NSCLC patients with documented BM from August 2007 to April 2015 of two hospitals were studied retrospectively.Cox regression was used for multivariate analysis.Survival of patients with or without postradiation systemic therapy was compared in subgroups stratified according to RTOG-RPA or GPA.Results：Of 216 included patients,67.1% received stereotactic radiosurgery （SRS）,24.1% received whole-brain radiation therapy （WBRT）,and 8.8% received both.After radiotherapy,systemic therapy was administered in 58.3% of patients.Multivariate analysis found that postradiation systemic therapy （yes vs.no） （hazard ratio [HR] =0.36 l,95% confidence interval [CI] =0.202-0.648,P =0.001）,radiation technique （SRS vs.WBRT） （HR =0.462,95% CI =0.238-0.849,P =0.022）,extracranial metastasis （yes vs.no） （HR =3.970,95% CI =1.757-8.970,P =0.001）,and Kamofsky performance status （〈70 vs.≥70） （HR =5.338,95% CI =2.829-10.072,P 〈 0.001） were independent factors for survival.Further analysis found that subsequent tyrosine kinase inhibitor （TKI） therapy could significantly reduce the risk of mortality of patients in RTOG-RPA Class IⅡ （HR =0.411,95% CI =0.183-）.923,P =0.031） or with a GPA score of 1.5-2.5 （HR =0.420,95% CI =0.182-0.968,P =0.042）.However,none of the subgroups stratified according to RTOG-RPA or GPA benefited from the additional conventional chemotherapy.Conclusion：RTOG-RPA and GPA may be useful to identify beneficial populations in NSCLC patients with BM ifTKIs were chosen as postradiation systemic therapy.
文摘Numerous previous literature has attempted to apply machine learning techniques to analyze relationships between energy variables in energy consumption.However,most machine learning methods are primarily used for prediction through complicated learning processes at the expense of interpretability.Those methods have difficulties in evaluating the effect of energy variables on energy consumption and especially capturing their heterogeneous relationship.Therefore,to identify the energy consumption of the heterogeneous relationships in actual buildings,this study applies the MOdel-Based recursive partitioning(MOB)algorithm to the 2012 CBECS survey data,which would offer representative information about actual commercial building characteristics and energy consumption.With resultant tree-structured subgroups,the MOB tree reveals the heterogeneous effect of energy variables and mutual influences on building energy consumptions.The results of this study would provide insights for architects and engineers to develop energy conservative design and retrofit in U.S.office buildings.
文摘Interior Alaska has a short growing season of 110 d.The knowledge of timings of crop flowering and maturity will provide the information for the agricultural decision making.In this study,six machine learning algorithms,namely Linear Discriminant Analysis(LDA),Support Vector Machines(SVMs),k-nearest neighbor(kNN),Naïve Bayes(NB),Recursive Partitioning and Regression Trees(RPART),and Random Forest(RF),were selected to forecast the timings of barley flowering and maturity based on the Alaska Crop Datasets and climate data from 1991 to 2016 in Fairbanks,Alaska.Among 32 models fit to forecast flowering time,two from LDA,12 from SVMs,four from NB,three from RF outperformed models from other algorithms with the highest accuracy.Models from kNN performed worst to forecast flowering time.Among 32 models fit to forecast maturity time,two models from LDA outperformed the models from other algorithms.Models from kNN and RPART performed worst to forecast maturity time.Models from machine learning methods also provided a variable importance explanation.In this study,four out of six algorithms gave the same variable importance order.Sowing date was the most important variable to forecast flowering but less important variable to forecast maturity.The daily maximum temperature may be more important than daily minimum temperature to fit flowering models while daily minimum temperature may be more important than daily maximum temperature to fit maturity models.The results indicate that models from machine learning provide a promising technique in forecasting the timings of flowering and maturity of barley.
文摘Background: The vasoactive-ventilation-renal (VVR) score includes pulmonary and renal dysfunctions not previously addressed by the vasoactive inotrope score (VIS) and may be a better predictor of cardiac care unit (CCU) length of stay (LOS) in patients undergoing re-entry sternotomy (defined as no earlier than 30 days after previous sternotomy) for congenital heart disease (CHD). Methods: Patients undergoing re-entry sternotomy for CHD from August 1, 2009 to June 30, 2016 were studied retrospectively. A total of 96 patients undergoing 133 re-entry procedures were identified. VVR scores were calculated on CCU admission post-procedure (at 0 hour), 24-hour, and 48-hour after admission to the CCU. The response variable was CCU LOS.? Recursive partition analysis identified variables predicting LOS. Results: 133 re-entry sternotomies in 96 patients made up the samples of the database;11 samples were removed due to incomplete data or placement on ECMO. Of the initial 25 features, 5 were removed for near zero variance and 3 categorical features were removed for non-information. Covariance analysis did not demonstrate any significant correlation amongst the remaining features. Initial recursive tree regression using ANOVA, cross validation and conditional predictive p-value (cp) = 0.01 produced 3 trees. The tree with lowest cross validation error was selected. The resulting 2 split trees with ventilator days less than 20 days and VVR score at 48 hours greater than 23 identified three CCU LOS groups with mean CCU LOS of 77.6, 55.1, and 9.5 days. Conclusions: Recursive partition analysis identified ventilator days greater than 20 days and the sub-population VVR at 48 hours as predictive of CCU LOS in patients undergoing re-entry sternotomy for CHD.
