Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India.
Indian Institute of Information Technology, Sonepat, India.
J Biomed Inform. 2022 Apr;128:104026. doi: 10.1016/j.jbi.2022.104026. Epub 2022 Feb 12.
Data mining and machine learning techniques are transforming the decision-making process in the medical world. From using nomograms and expert advice, scientists are now moving towards machine learning and deep learning techniques to make informed decisions for patients. The change in this aspect is mainly attributed to large amounts of digital data stored in hospitals. This study is focused on the transformation of cancer survival research in the past few years. A road map based on seven different aspects has been provided in this study utilizing various machine learning techniques, presenting a review of 62 articles published in the past 15 years. It was found that researchers are now moving to more clinical data even with less number of instances. Though most of the studies used traditional machine learning techniques for predicting cancer survival, researchers are now moving towards deep learning and hybrid approaches to gain some insights into survival prediction. Finally, this study presents ten new open research issues and possible future research plans to focus on for better results in cancer survival research. It is hoped that this review will be viewed by both apprentice and expert researchers as a valuable resource to understand the currently used practices and possible future recommendations to work.
数据挖掘和机器学习技术正在改变医学领域的决策过程。科学家们正在从使用诺莫图和专家建议转向机器学习和深度学习技术，以便为患者做出明智的决策。这方面的变化主要归因于医院中存储的大量数字数据。本研究主要关注过去几年癌症生存研究的转变。本研究利用各种机器学习技术提供了基于七个不同方面的路线图，对过去 15 年发表的 62 篇文章进行了综述。研究发现，研究人员现在即使在实例较少的情况下，也倾向于使用更多的临床数据。虽然大多数研究都使用传统的机器学习技术来预测癌症的生存情况，但研究人员现在正在转向深度学习和混合方法，以深入了解生存预测。最后，本研究提出了十个新的开放研究问题和可能的未来研究计划，以专注于癌症生存研究中获得更好的结果。希望本综述能为新手和专家研究人员提供有价值的资源，以了解当前的实践和可能的未来建议。
