Application of machine learning method for survival analysis
LIU Yue1,2, LIU Qiling1, SU Haixia2,3, YANG Peng2,3, ZHANG Yuhai2,3
1. School of Public Health, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi 712046, China; 2. Department of Military Preventive Medicine, Air Force Medical University, Xi'an, Shaanxi 710032, China; 3. Ministry of Education Key Laboratory of Environmental Hazard Assessment and Prevention in Special Operation, Xi'an, Shaanxi 710032, China
Abstract：Survival analysis has been widely used in the field of medical research. The Cox proportional hazard model is commonly used, but its practical application is limited. Machine learning method can compensate for the shortcomings of the Cox proportional hazard model in terms of nonlinear data processing and prediction accuracy. This article reviewed the advance of machine learning methods represented by neural networks, within the field of survival analysis, and highlighted the principles and benefits of three machine learning methods that DeepSurv, Deep-Hit and random survival forest, providing methodological insights for the analysis of complex survival data.
[1] FARAGGI D,SIMON R.A neural network model for survival data[J].Stat Med,1995,14(18):73-82. [2] SRIVASTAVA N,HINTON G E,KRIZHEVSKY A,et al.Dropout:a simple way to prevent neural networks from overfitting[J].J Mach Learn Res,2015,15(1):1929-1958. [3] 邹军华,段晔鑫,任传伦,等.基于噪声初始化、Adam-Nesterov方法和准双曲动量方法的对抗样本生成方法[J].电子学报,2022,50(1):207-216. [4] 李文琦,黄水平,李海朋.BP神经网络和Cox比例风险模型在生存分析应用中的比较[J].郑州大学学报,2014,49(6):822-825. [5] XIANG A,LAPUERTA P,RYUTOV A,et al.Comparison of the performance of neural network methods and Cox regression for censored survival data[J].Comput Stat Data An,2000,34(2):243-257. [6] SARGENT D J.Comparison of artificial neural networks with other statistical approaches:results from medical data sets[J].Cancer,2001,91(8):1636-1642. [7] KATZMAN J L,SHAHAM U,CLONINGER A,et al.DeepSurv:personalized treatment recommender system using a Cox proportional hazards deep neural network[J/OL].BMC Med Res Methodol,2018,18(1)[2024-05-10].https://doi.org/10.1186/s12874-018-0482-1. [8] CHING T,ZHU X,GARMIRE L X.Cox-nnet:an artificial neural network method for prognosis prediction of high-throughput omics data[J/OL].PLoS Comput Biol,2018,14(4)[2024-05-10].https://doi.org/10.1371/journal.pcbi.1006076. [9] ZHU X,YAO J,ZHU F,et al.Wsisa:making survival prediction from whole slide histopathological lmages[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR),2017:6855-6863. [10] YOUSEFI S,AMROLLAHI F,AMGAD M,et al.Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models[J/OL].Sci Rep,2017,7(1)[2024-05-10].https://doi.org/10.1038/s41598-017-11817-6. [11] LUCK M,SYLVAIN T,CARDINAL H,et al.Deep learning for patient-specific kidney graft survival analysis[J/OL].arXiv,2017[2024-05-10].https://doi.org/10.48550/arXiv.1705.10245. [12] FOTSO S.Deep neural networks for survival analysis based on a multi-task framework[J/OL].arXiv,2018[2024-05-10].https://doi.org/10.48550/arXiv.1801.05512. [13] LEE C,ZAME W R,YOON J,et al.DeepHit:a deep learning approach to survival analysis with competing risks[C]//Thirty-Second AAAI Conference on Artificial Intelligence New Orleans,2018:2314-2321. [14] ISHWARAN H,KOGALUR U B,BLACKSTONE E H,et al.Random survival forests[J/OL].Ann Appl Stat,2008,2(3)[2024-05-10].https://doi.org/10.1214/08-AOAS169. [15] BIGLARIAN A,BAKHSHI E,GOHARI M R,et al.Artificial neural network for prediction of distant metastasis in colorectal cancer[J].APJCP,2012,13(3):927-930. [16] RAU H H,HSU C Y,LIN Y A,et al.Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network[J].Comput Methods Programs Biomed,2016,125(4):58-65. [17] YU H H,HUANG T,FENG B,et al.Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance,epidemiology,and end results analysis[J/OL].BMC Cancer,2022,22(13)[2024-05-10].https://doi.org/10.1186/s12885-022-09217-9. [18] YU H,YANG W,WU S,et al.Deep-learning-based survival prediction of patients with cutaneous malignant melanoma[J/OL].Front Med(Lausanne),2023,10[2024-05-10].https://doi.org/10.3389/fmed.2023.1165865. [19] ADEOYE J,KOOHI-MOGHADAM M,LO A W I,et al.Deep learning predicts the malignant-transformation-free survival of oral potentially malignant disorders[J/OL].Cancers(Basel),2021,13(23)[2024-05-10].https://doi.org/10.3390/cancers13236054. [20] SKREDE O J,DE RAEDT S,KLEPPE A,et al.Deep learning for prediction of colorectal cancer outcome:a discovery and validation study[J].Lancet,2020,395(10221):350-360. [21] KIM B,JANG Y J,CHO H R,et al.Predicting completion of clinical trials in pregnant women:Cox proportional hazard and neural network models[J].Clin Transl Sci,2022,15(3):691-699. [22] WANG F F,CHEN L,LIU L H,et al.Deep learning model for predicting the survival of patients with primary gastrointestinal lymphoma based on the seer database and a multicentre external validation cohort[J].J Cancer Res Clin Oncol,2023,149(13):12177-12189. [23] JIANG C,WANG K,YAN L Z,et al.Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning:a study based on surveillance,epidemiology,and end results database[J].Cancer Med,2023,12(11):12413-12424. [24] KIM D Y,CHOI K H,KIM J H,et al.Deep learning-based personalised outcome prediction after acute ischaemic stroke[J].J Neurol Neurosurg Psychiatry,2023,94(5):369-378. [25] BAE C Y,KIM B S,JEE S H,et al.A study on survival analysis methods using neural network to prevent cancers[J/OL].Cancers,2023,15(19)[2024-05-10].https://doi.org/10.3390/cancers15194757. [26] ADEOYE J,HUI L L,KOOHI-MOGHADAM M,et al.Comparison of time-to-event machine learning models in predicting oral cavity cancer prognosis[J/OL].Int J Med Inform,2022,157[2024-05-10].https://doi.org/10.1016/j.ijmedinf.2021.104635. [27] LIN J,LI K,LUO S.Functional survival forests for multivariate longitudinal outcomes:dynamic prediction of Alzheimer's disease progression[J].Stat Methods Med Res,2021,30(1):99-111. [28] KIM D W,LEE S,KWON S,et al.Deep learning-based survival prediction of oral cancer patients[J/OL].Sci Rep,2019,9(1)[2024-05-10].https://doi.org/10.1038/s41598-019-43372-7. [29] BYUN S S,HEO T S,CHOI J M,et al.Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma[J/OL].Sci Rep,2021,11(1)[2024-05-10].https://doi.org/10.1038/s41598-020-80262-9. [30] WANG D,PAN B,HUANG J C,et al.Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy:a real-world study of distal cholangiocarcinoma[J/OL].Front Oncol,2023,13(2)[2024-05-10].https://doi.org/10.3389/fonc.2023.1106029. [31] 陈雨轩,韦红霞,潘建红,等.扩展Cox模型在非线性生存资料分析中的预测能力比较[J].南方医科大学学报,2023,43(1):76-84. [32] 张梦,张利,刘梦雅,等.老年人自评健康状况与死亡风险的关联分析[J].预防医学,2024,36(2):105-108. [33] 李嵘,张文丽,李扬,等.基于深度学习的大规模肿瘤数据生存分析[J].中国卫生统计,2022,39(1):84-90.
