MORTALITY PREDICTION OF SURGICAL INTENSIVE CARE UNIT PATIENTS USING DEEP LEARNING-BASED SURVIVAL MODELS
Article Sidebar
Main Article Content
Abstract
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.
Downloads
Article Details
Transfer of Copyrights
- In the event of publication of the manuscript entitled [INSERT MANUSCRIPT TITLE AND REF NO.] in the Malaysian Journal of Science, I hereby transfer copyrights of the manuscript title, abstract and contents to the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) for the full legal term of copyright and any renewals thereof throughout the world in any format, and any media for communication.
Conditions of Publication
- I hereby state that this manuscript to be published is an original work, unpublished in any form prior and I have obtained the necessary permission for the reproduction (or am the owner) of any images, illustrations, tables, charts, figures, maps, photographs and other visual materials of whom the copyrights is owned by a third party.
- This manuscript contains no statements that are contradictory to the relevant local and international laws or that infringes on the rights of others.
- I agree to indemnify the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) in the event of any claims that arise in regards to the above conditions and assume full liability on the published manuscript.
Reviewer’s Responsibilities
- Reviewers must treat the manuscripts received for reviewing process as confidential. It must not be shown or discussed with others without the authorization from the editor of MJS.
- Reviewers assigned must not have conflicts of interest with respect to the original work, the authors of the article or the research funding.
- Reviewers should judge or evaluate the manuscripts objective as possible. The feedback from the reviewers should be express clearly with supporting arguments.
- If the assigned reviewer considers themselves not able to complete the review of the manuscript, they must communicate with the editor, so that the manuscript could be sent to another suitable reviewer.
Copyright: Rights of the Author(s)
- Effective 2007, it will become the policy of the Malaysian Journal of Science (published by the Faculty of Science, University of Malaya) to obtain copyrights of all manuscripts published. This is to facilitate:
- Protection against copyright infringement of the manuscript through copyright breaches or piracy.
- Timely handling of reproduction requests from authorized third parties that are addressed directly to the Faculty of Science, University of Malaya.
- As the author, you may publish the fore-mentioned manuscript, whole or any part thereof, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given. You may produce copies of your manuscript, whole or any part thereof, for teaching purposes or to be provided, on individual basis, to fellow researchers.
- You may include the fore-mentioned manuscript, whole or any part thereof, electronically on a secure network at your affiliated institution, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- You may include the fore-mentioned manuscript, whole or any part thereof, on the World Wide Web, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- In the event that your manuscript, whole or any part thereof, has been requested to be reproduced, for any purpose or in any form approved by the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers), you will be informed. It is requested that any changes to your contact details (especially e-mail addresses) are made known.
Copyright: Role and responsibility of the Author(s)
- In the event of the manuscript to be published in the Malaysian Journal of Science contains materials copyrighted to others prior, it is the responsibility of current author(s) to obtain written permission from the copyright owner or owners.
- This written permission should be submitted with the proof-copy of the manuscript to be published in the Malaysian Journal of Science
References
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