检索结果-南通市图书馆

Prognostic prediction models for postoperative patients with stageⅠtoⅢcolorectal cancer based on machine learning

World Journal of Gastrointestinal Oncology 2024年第12期16卷 4597-4613页

作者： Xiao-Lin Ji Shuo Xu Xiao-Yu Li Jin-Huan Xu Rong-Shuang Han Ying-Jie Guo Li-Ping Duan Zi-Bin Tian Department of Gastroenterology Beijing Key Laboratory for Helicobacter Pylori Infection and Upper Gastrointestinal DiseasesPeking University Third HospitalBeijing 100191China Beijing Aerospace Wanyuan Science Technology Co. Ltd.China Academy of Launch Vehicle TechnologyBeijing 100176China Department of Gastroenterology The Affiliated Hospital of Qingdao UniversityQingdao 266003Shandong ProvinceChina Institute of Automation Qilu University of Technology(Shandong Academy of Sciences)Jinan 250014Shandong ProvinceChina

BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality *** machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting *** addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical *** To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢ*** More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap *** variables were screened with multivariable regression *** performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model *** were constructed for practical model *** Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was *** highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free *** cross-validation and bootstrap validation were performed in both the training and testing *** SHAP values of the important va

关键词： Colorectal cancer Machine learning Prognostic prediction model survival times Important variables

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An Adaptive Approach for Hazard Regression Modeling

引用

Open Journal of Statistics 2023年第3期13卷 300-315页

作者： George J. Knafl School of Nursing University of North Carolina at Chapel Hill Chapel Hill USA

Regression models for survival time data involve estimation of the hazard rate as a function of predictor variables and associated slope parameters. An adaptive approach is formulated for such hazard regression modeling. The hazard rate is modeled using fractional polynomials, that is, linear combinations of products of power transforms of time together with other available predictors. These fractional polynomial models are restricted to generating positive-valued hazard rates and decreasing survival times. Exponentially distributed survival times are a special case. Parameters are estimated using maximum likelihood estimation allowing for right censored survival times. Models are evaluated and compared using likelihood cross-validation (LCV) scores. LCV scores and tolerance parameters are used to control an adaptive search through alternative fractional polynomial hazard rate models to identify effective models for the underlying survival time data. These methods are demonstrated using two different survival time data sets including survival times for lung cancer patients and for multiple myeloma patients. For the lung cancer data, the hazard rate depends distinctly on time. However, controlling for cell type provides a distinct improvement while the hazard rate depends only on cell type and no longer on time. Furthermore, Cox regression is unable to identify a cell type effect. For the multiple myeloma data, the hazard rate also depends distinctly on time. Moreover, consideration of hemoglobin at diagnosis provides a distinct improvement, the hazard rate still depends distinctly on time, and hemoglobin distinctly moderates the effect of time on the hazard rate. These results indicate that adaptive hazard rate modeling can provide unique insights into survival time data.

关键词： Adaptive Regression Fractional Polynomials Hazard Rate Likelihood Cross-Validation survival times

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：