Data Science Institute, NUI Galway, Galway, Ireland.
Insight Centre for Data Analytics, NUI Galway, Galway, Ireland.
AMIA Annu Symp Proc. 2022 Feb 21;2021:853-862. eCollection 2021.
Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients. We discuss the promising results achieved, as well as the lessons we learned while developing this baseline for further, more advanced studies in this area.
早期发现和减轻非小细胞肺癌（NSCLC）患者的疾病复发是一个重要问题，通常通过通用的随访筛查指南、自我报告、简单的列线图或使用回顾性数据分析来预测个体患者复发风险的统计模型来解决。我们假设，基于患者数据训练的机器学习模型可以提供一种替代方法，能够同时更有效地开发许多互补的模型，具有更高的准确性，对数据收集协议的依赖性更低，并提高预测的可解释性。在这项初步研究中，我们描述了一套应用于 2442 例早期 NSCLC 患者队列的各种机器学习模型的实验套件。我们讨论了所取得的有希望的结果，以及在开发该基线以进行该领域更先进的研究时所吸取的经验教训。
