使用机器学习、一般回归和Cox比例风险回归来预测乳腺癌患者的治疗效果。
Using machine learning, general regression, and Cox proportional hazards regression to predict the effectiveness of treatment in patients with breast cancer.
作者信息
Wang Xiaoyan, Hershman Dawn L, Neugut Alfred I
机构信息
Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
出版信息
AMIA Annu Symp Proc. 2006;2006:1133.
The objective of this feasibility study is to introduce machine learning algorithms in the combination of general regression and cox proportional hazards regression to predicate the outcome of disease management. By using the delay in the receipt of adjuvant chemotherapy and SEER-Medicare databases as proof-of-principle, we conclude that general regression and Cox proportional hazards regression following the feature selection could identify factors that predict the delay and the impact of delay on survival outcome.
这项可行性研究的目的是将机器学习算法引入一般回归和Cox比例风险回归的组合中，以预测疾病管理的结果。通过使用辅助化疗延迟情况以及SEER-Medicare数据库作为原理验证，我们得出结论，经过特征选择后的一般回归和Cox比例风险回归能够识别出预测延迟以及延迟对生存结果影响的因素。
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