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| Application of machine learning method for survival analysis |
| LIU Yue1,2, LIU Qiling1, SU Haixia2,3, YANG Peng2,3, ZHANG Yuhai2,3
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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 |
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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.
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Received: 23 February 2024
Revised: 10 May 2024
Published: 20 June 2024
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