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Lintu M.K.
S.N.S. Acharya
Asha Kamath
David Raj Micheal


Mortality prediction in surgical intensive care units (SICUs) is considered to be among the most critical steps in enforcing efficient treatment policies. This study aims to evaluate the performance of various deep learning models in predicting the mortality of patients admitted to SICUs. The survival of 2,225 adult patients admitted to SICUs was modeled using five salient deep learning-based survival models, namely, Cox-CC, Cox-Time, DeepSurv, DeepHit, and N-MTLR. The data were extracted from the Medical Information Mart for Intensive Care II (MIMIC-II) database. The performance of the models was compared using the time-dependent concordance index (Ctd-index) and integrated Brier score (IBS). From among the five models, DeepSurv achieved the most accurate prediction, while Cox-Time demonstrated the least optimal predictive ability. For DeepSurv, Cox-CC, DeepHit, N-MTLR, and Cox-Time, the mean Ctd -index was 0.773, 0.767, 0.765, 0.732, and 0.659, and the mean IBS was 0.181, 0.192, 0.195, 0.212, and 0.225, respectively. DeepSurv, Cox-CC, and DeepHit yielded comparable performance. Deep learning models are free from the stringent assumptions inherent in standard survival models. Hence, these models are considered flexible alternatives to the standard approaches in scalable, real-world survival problems.


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M.K., L., Acharya, S., Kamath, A., & Micheal, D. R. (2022). MORTALITY PREDICTION OF SURGICAL INTENSIVE CARE UNIT PATIENTS USING DEEP LEARNING-BASED SURVIVAL MODELS. Malaysian Journal of Science, 41(3), 44–48.
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Antolini, L., Boracchi, P., & Biganzoli, E. (2005). A time-dependent discrimination index for survival data. Statistics in medicine 24: 3927-3944.

Austin, P. C., Rothwell, D. M., & Tu, J. V. (2002). A Comparison of Statistical Modeling Strategies for Analyzing Length of Stay after CABG Surgery. Health Services and Outcomes Research Methodology 3: 107-133.

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online] 101, pp. e215–e220.

Graf, E., Schmoor, C., Sauerbrei, W., & Schumacher, M. (1999). Assessment and comparison of prognostic classification schemes for survival data. Statistics in medicine 18: 2529-2545.

Hartl, W. H., Wolf, H., Schneider, C. P., Küchenhoff, H., & Jauch, K. W. (2007). Acute and long-term survival in chronically critically ill surgical patients: a retrospective observational study. Critical Care 11: 1-11.

Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp. 448-456.

Jalali, A., Lonsdale, H., Do, N., Peck, J., Gupta, M., Kutty, S., ... & Ahumada, L. M. (2020). Deep Learning for Improved Risk Prediction in Surgical Outcomes. Scientific Reports 10: 9289-9302.

Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology 18: 24-36.

Kvamme, H., Borgan, Ø., & Scheel, I. (2019). Time-to-event prediction with neural networks and Cox regression. Journal of Machine Learning Research 20: 1-30.

Lee, C., Zame, W. R., Yoon, J., & van der Schaar, M. (2018). Deephit: A deep learning approach to survival analysis with competing risks. In: Thirty-second AAAI conference on artificial intelligence.

Mosissa, D., Alemu, S., Rad, M. H., & Yesuf, E. A. (2021). Outcomes of Surgical Patients Admitted to the Intensive Care Unit of Jimma University Medical Center. Health Science Journal 15: 1-4.

Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In: 27th International Conference on Machine Learning: USA. Omnipress, pp. 807-814.

Prechelt, L. (1998). Early stopping-but when?. In: Neural Networks: Tricks of the trade, pp. 55-69. Springer, Berlin, Heidelberg.

Rafsunjani, S., Safa, R. S., Al Imran, A., Rahim, M. S., & Nandi, D. (2019). An empirical comparison of missing value imputation techniques on APS failure prediction. International Journal of Information Technology and Computer Science 2: 21-29.

Sargent, D. J. (2001). Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer: Interdisciplinary International Journal of the American Cancer Society 91: 1636-1642.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15: 1929-1958.

Wang, P., Li, Y., & Reddy, C. K. (2019). Machine learning for survival analysis: A survey. ACM Computing Surveys 51: 1-36.

Xiang, A., Lapuerta, P., Ryutov, A., Buckley, J., & Azen, S. (2000). Comparison of the performance of neural network methods and Cox regression for censored survival data. Computational statistics & data analysis 34: 243-257.

Yu, C. N., Greiner, R., Lin, H. C., & Baracos, V. (2011). Learning patient-specific cancer survival distributions as a sequence of dependent regressors. Advances in neural information processing systems 24: 1845–1853.

Yun, K., Oh, J., Hong, T. H., & Kim, E. Y. (2021). Prediction of Mortality in Surgical Intensive Care Unit Patients Using Machine Learning Algorithms. Frontiers in Medicine 8: 406-415