Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
J Am Med Inform Assoc. 2021 Jun 12;28(6):1108-1116. doi: 10.1093/jamia/ocaa290.
Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model.
A machine learning model was trained using 14 600 metastatic cancer patients' data to predict each patient's distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015-2016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots.
The radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63-0.81), 0.77 (0.73-0.81), and 0.68 (0.65-0.71), respectively.
The machine learning model's predictions were more accurate than those of the treating physician or a traditional model.
能够预测患者的预期寿命有助于医生和患者优先考虑治疗和支持性护理。对于预测预期寿命，已经证明医生的表现优于仅使用少数预测变量的传统模型。使用来自电子病历的许多预测变量和不同数据源的机器学习模型有可能改善医生的表现。对于转移性癌症患者，我们比较了主治医生、机器学习模型和传统模型预测预期寿命的准确性。
使用来自 14600 名转移性癌症患者的数据训练机器学习模型，以预测每位患者的生存时间分布。数据源包括笔记文本、实验室值和生命体征。在 2015-2016 年期间，招募了 899 名接受转移性癌症放射治疗的患者参加一项研究，其中他们的放射肿瘤学家估计了预期寿命。机器学习模型和仅使用表现状态的传统模型也进行了生存预测。使用 1 年生存率的曲线下面积和校准图评估性能。
放射治疗研究包括 899 名患者的 1190 个治疗疗程。共有 685 名患者的 879 个治疗疗程纳入了本分析。中位总生存期为 11.7 个月。医生、机器学习模型和传统模型的 1 年生存率的曲线下面积分别为 0.72（95%CI 0.63-0.81）、0.77（0.73-0.81）和 0.68（0.65-0.71）。
机器学习模型的预测比主治医生或传统模型更准确。
