Yuan Yue, Zhang Guolong, Gu Yuqi, Hao Sicheng, Huang Chen, Xie Hongxia, Mi Wei, Zeng Yingchun
School of Nursing, Hunan University of Medicine, Huaihua, China.
Respiratory Intervention Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Asia Pac J Oncol Nurs. 2025 Mar 7;12:100680. doi: 10.1016/j.apjon.2025.100680. eCollection 2025 Dec.
This study aimed to evaluate the feasibility of large language model-Advanced Data Analysis (ADA) in developing and implementing machine learning models to predict survival outcomes for lung cancer patients, with a focus on its implications for nursing practice.
A retrospective study design was employed using a dataset of lung cancer patients. Data included sociodemographic, clinical, treatment-specific, and comorbidity variables. Large language model-ADA was used to build and evaluate three machine learning models. Model performance was validated, and results were presented using calibration plots.
Of 737 patients, the survival rate of this cohort was 73.3%, with a mean age of 59.32 years. Calibration plots indicated robust model reliability across all models. The Random Forest model demonstrated the highest predictive accuracy among the models. Most critical features identified were preoperative white blood cells (2.2%), preoperative lung function of Forced Expiratory Volume in one second (2.1%), preoperative arterial oxygen saturation (1.9%), preoperative partial pressure of oxygen (1.7%), preoperative albumin (1.6%), preoperative preparation time (1.5%), age at admission (1.5%), preoperative partial pressure of carbon dioxide (1.5%), preoperative hospital stay days (1.5%), and postoperative total days of thoracic tube drainage (1.4%).
Large language model-ADA effectively facilitates the development of machine learning models for lung cancer survival prediction, enabling non-technical health care professionals to harness the power of advanced analytics. The findings underscore the importance of preoperative factors in predicting outcomes, while also highlighting the need for external validation across diverse settings.
本研究旨在评估大语言模型-高级数据分析（ADA）在开发和实施机器学习模型以预测肺癌患者生存结局方面的可行性，重点关注其对护理实践的影响。
采用回顾性研究设计，使用肺癌患者数据集。数据包括社会人口统计学、临床、治疗特异性和合并症变量。大语言模型-ADA用于构建和评估三种机器学习模型。对模型性能进行验证，并使用校准图呈现结果。
在737例患者中，该队列的生存率为73.3%，平均年龄为59.32岁。校准图表明所有模型的模型可靠性都很强。随机森林模型在各模型中显示出最高的预测准确性。确定的最关键特征为术前白细胞（2.2%）、术前一秒用力呼气量肺功能（2.1%）、术前动脉血氧饱和度（1.9%）、术前氧分压（1.7%）、术前白蛋白（1.6%）、术前准备时间（1.5%）、入院年龄（1.5%）、术前二氧化碳分压（1.5%）、术前住院天数（1.5%）和术后胸腔闭式引流总天数（1.4%）。
大语言模型-ADA有效地促进了用于肺癌生存预测的机器学习模型的开发，使非技术型医疗保健专业人员能够利用高级分析的力量。研究结果强调了术前因素在预测结局方面的重要性，同时也突出了在不同环境中进行外部验证的必要性。
