Machine Learning Algorithms for Survival Analysis: Advantages, Disadvantages, and Examples
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Published:2024-01-05
Issue:1
Volume:4
Page:10-21
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ISSN:2789-2557
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Container-title:International Journal of Artificial Intelligence and Machine Learning
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language:
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Short-container-title:IJAIML
Reference48 articles.
1. Alyass, A., Turcotte, M. and Meyre, D. (2015). From Big Data Analysis to Personalized MedicinefFor All: Challenges and Opportunities. BMC Med Genomics, 8(1), 33. doi: 10.1186/s12920-015-0108-y. 2. Azodi, C.B., Tang, J. and Shiu, S.H. (2020). Opening the Black Box: Interpretable Machine Learning for Geneticists. Trends in Genetics, 36(6), 442-455. Elsevier Ltd. doi: 10.1016/j.tig.2020.03.005. 3. Barnwal, A., Cho, H. and Hocking, T. (2022). Survival Regression with Accelerated Failure Time Model in XGBoost. Journal of Computational and Graphical Statistics, 31(4), 1292-1302. doi: 10.1080/10618600.2022.2067548. 4. Barrett, J.K., Siannis, F. and Farewell, V.T. (2011). A Semi-competing Risks Model for Data With Interval-censoring and Informative Observation: An Application to the MRC Cognitive Function And Ageing Study. Stat Med, 30(1), 1-10. doi: 10.1002/sim.4071. 5. Basak, P., Linero, A., Sinha, D. and Lipsitz, S. (2022). Semiparametric Analysis of Clustered Interval-censored Survival Data Using Soft Bayesian Additive Regression Trees (SBART). Biometrics, 78(3), 880-893. doi: 10.1111/biom.13478.
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