A Kernel Attention-based Transformer Model for Survival Prediction of Heart Disease Patients

Altman DG, Bland JM. Time to event (survival) data. BMJ. 1998;317(7156):468–9. https://doi.org/10.1136/bmj.317.7156.468, https://www.ncbi.nlm.nih.gov/pubmed/9703534

Archetti A, Matteucci MJ. Federated survival forests. arXiv:2302.02807 (2023)

Aslan MF, Sabanci K, Durdu A. A cnn-based novel solution for determining the survival status of heart failure patients with clinical record data: numeric to image. Biomed Signal Process Control. 2021;68:102716.

Article  Google Scholar 

Binder H, Schumacher M. Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics. 2008;9:1–10.

Article  Google Scholar 

Chicco D, Jurman G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform and making, decision. 2020;20(1):1–16.

Article  Google Scholar 

Cox DR. Regression models and life-tables. J Roy Stat Soc B. 1972;34(2):187–202.

Article  Google Scholar 

Davide Chicco GJ. Heart failure clinical records. UCI Machine Learning Repository; 2020. https://doi.org/10.24432/C5Z89R

Dehghani M, Gouws S, Vinyals O, et al. Universal transformers. arXiv:03819 (2018)

Drysdale E. Survset: an open-source time-to-event dataset repository. arXiv:2203.03094

Gensheimer MF, Narasimhan B. A scalable discrete-time survival model for neural networks. PeerJ. 2019;7:e6257.

Article  PubMed Central  Google Scholar 

Geva M, Schuster R, Berant J, et al. Transformer feed-forward layers are key-value memories. arXiv:2012.14913 (2020)

Goel MK, Khanna P, Kishore J. Understanding survival analysis: Kaplan-meier estimate. Int J Ayurveda Res. 2010;1(4):274.

Article  PubMed Central  Google Scholar 

Gottdiener JS, Arnold AM, Aurigemma GP, et al. Predictors of congestive heart failure in the elderly: the cardiovascular health study. J Am Coll Cardiol. 2000;35(6):1628–37.

Article  CAS  Google Scholar 

Graves A. Adaptive computation time for recurrent neural networks. arXiv:08983 (2016)

Harrell FEJr, Harrell FE. Cox proportional hazards regression model. Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis; 2015. pp. 475–519

Harrell JFE, Califf RM, Pryor DB, et al. Evaluating the yield of medical tests. JAMA 1982;247(18):2543–6. https://www.ncbi.nlm.nih.gov/pubmed/7069920

Hu S, Fridgeirsson E, van Wingen G, et al. Transformer-based deep survival analysis. In: Survival prediction-algorithms, challenges and applications. PMLR; 2021. pp. 132–148

Ishwaran H, Kogalur UB, Blackstone EH, et al. Random survival forests. The Annals of Applied Statistics; 2008

Jørgensen HS, Nakayama H, Reith J, et al. Acute stroke with atrial fibrillation: the copenhagen stroke study. Stroke 1996;27(10):1765–9

Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958;53(282):457–81.

Article  Google Scholar 

Katzman JL, Shaham U, Cloninger A, et al. Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18(1):24. https://doi.org/10.1186/s12874-018-0482-1, https://www.ncbi.nlm.nih.gov/pubmed/29482517

Kenchaiah S, Narula J, Vasan RS. Risk factors for heart failure. Medical Clinics. 2004;88(5):1145–72.

Google Scholar 

Kopper P, Wiegrebe S, Bischl B, et al. Deeppamm: deep piecewise exponential additive mixed models for complex hazard structures in survival analysis. In: Pacific-Asia conference on knowledge discovery and data mining. Springer; 2022. pp. 249–261.

Lagakos SW. General right censoring and its impact on the analysis of survival data. Biometrics. 1979;35(1):139–56. https://www.ncbi.nlm.nih.gov/pubmed/497332

Lee C, Zame W, Yoon J, et al. Deephit: a deep learning approach to survival analysis with competing risks. In: Proceedings of the AAAI conference on artificial intelligence; 2018. pp. 2316–2321

Lin J, Luo S. Deep learning for the dynamic prediction of multivariate longitudinal and survival data. Stat Med. 2022;41(15):2894–907. https://doi.org/10.1002/sim.9392, https://www.ncbi.nlm.nih.gov/pubmed/35347750

Nagpal C, Li X, Dubrawski A. Deep survival machines: fully parametric survival regression and representation learning for censored data with competing risks. IEEE J Biomed Health Inform. 2021;25(8):3163–75. https://doi.org/10.1109/JBHI.2021.3052441, https://www.ncbi.nlm.nih.gov/pubmed/33460387

Nagpal C, Yadlowsky S, Rostamzadeh N, et al. Deep cox mixtures for survival regression. In: Machine learning for healthcare conference. PMLR; 2021b. pp. 674–708

Nagpal C, Potosnak W, Dubrawski A. auton-survival: an open-source package for regression, counterfactual estimation, evaluation and phenotyping with censored time-to-event data. In: Machine learning for healthcare conference. PMLR; 2022. pp. 585–608

Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10); 2010. pp. 807–814

Palak K, Shailendra S, Dharam V. Recent trends in survival analysis using deep learning in medical science: current perspective and future direction. Neuroquantology. 2022;20(9):3330–3336. https://doi.org/10.14704/nq.2022.20.9.NQ44384

Qin Z, Sun W, Deng H, et al. cosformer: rethinking softmax in attention. arXiv:2202.08791 (2022)

Ren K, Qin J, Zheng L, et al. Deep recurrent survival analysis. In: Proceedings of the AAAI conference on artificial intelligence; 2019. pp. 4798–4805

Tang EW, Wong CK, Herbison P. Global registry of acute coronary events (grace) hospital discharge risk score accurately predicts long-term mortality post acute coronary syndrome. Am Heart J. 2007;153(1):29–35.

Article  Google Scholar 

Valenzuela TD, Roe DJ, Cretin S, et al. Estimating effectiveness of cardiac arrest interventions: a logistic regression survival model. Circulation. 1997;96(10):3308–13. https://doi.org/10.1161/01.cir.96.10.3308, https://www.ncbi.nlm.nih.gov/pubmed/9396421

Van Belle V, Pelckmans K, Van Huffel S, et al. Support vector methods for survival analysis: a comparison between ranking and regression approaches. Artif Intell Med. 2011;53(2):107–18.

Article  Google Scholar 

Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30

Wang Z, Sun J. Survtrace: transformers for survival analysis with competing events. In: Proceedings of the 13th ACM international conference on bioinformatics, computational biology and health informatics; 2022, pp. 1–9

Wiegrebe S, Kopper P, Sonabend R, et al. Deep learning for survival analysis: a review. arXiv:2305.14961 (2023)

Zhao Y, Hong Q, Zhang X, et al. Bertsurv: bert-based survival models for predicting outcomes of trauma patients. arXiv preprint arXiv:10928 (2021)

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