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使用患者报告结局(PRO)测量并比较五种机器学习技术(MLT)预测肺癌无病生存期

Predicting Disease-Free Lung Cancer Survival Using Patient Reported Outcome (PRO) Measurements with Comparisons of Five Machine Learning Techniques (MLT).

作者信息

Sim Jin-Ah, Yun Young Ho

机构信息

Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.

Department of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea.

出版信息

Stud Health Technol Inform. 2019 Aug 21;264:1588-1589. doi: 10.3233/SHTI190548.

Abstract

The study was to develop the lung cancer patients' prediction model for predicting 5-year survival after completion of treatment by using Machine Learning Technology (MLT), adding patient reporting (PRO) measurements of lung cancer survivors to a variety of clinical parameters. Finally, the survival prediction models with the addition of lung cancer survivors' PRO measurements to the well-known clinical variables, based on diverse MLT, improved the predictive performance that explains 5-year disease-free lung cancer survival.

摘要

该研究旨在通过使用机器学习技术(MLT),在各种临床参数中加入肺癌幸存者的患者报告结局(PRO)测量值,开发用于预测肺癌患者治疗完成后5年生存率的预测模型。最后,基于不同的MLT,在著名的临床变量中加入肺癌幸存者的PRO测量值的生存预测模型,提高了解释肺癌5年无病生存的预测性能。

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