机器学习技术有望支持在医学诊断和预后预测方面。我们利用机器学习，基于340例前列腺癌患者的诊断年龄、外周血和尿液检测数据构建了一种新的预后预测模型。使用了随机生存森林(RSF)和生存树进行机器学习。在前列腺癌转移性患者的时间序列预后预测模型中，RSF模型相较于传统的Cox比例风险模型在大部分无进展生存期(PFS)、总生存期(OS)和癌症特异性生存期(CSS)中预测准确度更高。基于RSF模型，我们利用生存树结合治疗前乳酸脱氢酶(LDH)和治疗后120天的碱性磷酸酶(ALP)值创建了适用于临床的OS和CSS预后预测模型。机器学习考虑到多个特征的非线性和联合影响，可以提供有用的信息，用于在治疗干预之前预测细胞转移性前列腺癌的预后。在治疗开始后增加数据将允许更精确的患者预后风险评估，并有利于后续治疗的选择。 ©2023作者。

Machine learning technology is expected to support diagnosis and prognosis prediction in medicine. We used machine learning to construct a new prognostic prediction model for prostate cancer patients based on longitudinal data obtained from age at diagnosis, peripheral blood and urine tests of 340 prostate cancer patients. Random survival forest (RSF) and survival tree were used for machine learning. In the time-series prognostic prediction model for metastatic prostate cancer patients, the RSF model showed better prediction accuracy than the conventional Cox proportional hazards model for almost all time periods of progression-free survival (PFS), overall survival (OS) and cancer-specific survival (CSS). Based on the RSF model, we created a clinically applicable prognostic prediction model using survival trees for OS and CSS by combining the values of lactate dehydrogenase (LDH) before starting treatment and alkaline phosphatase (ALP) at 120 days after treatment. Machine learning provides useful information for predicting the prognosis of metastatic prostate cancer prior to treatment intervention by considering the nonlinear and combined impacts of multiple features. The addition of data after the start of treatment would allow for more precise prognostic risk assessment of patients and would be beneficial for subsequent treatment selection.© 2023. The Author(s).