Abstract
Intensive Care Units (ICUs) are one of the most essential, but expensive healthcare services provided in hospitals. Modern monitoring machines in critical care units continuously generate huge amount of data, which can be used for intelligent decision-making. Prediction of mortality risk of patients is one such predictive analytics application, which can assist hospitals and healthcare personnel in making informed decisions. Traditional scoring systems currently in use are parametric scoring methods which often suffer from low accuracy. In this paper, an empirical study on the effect of feature selection on the feature set of traditional scoring methods for modeling an optimal feature set to represent each patient’s profile along with a supervised learning approach for ICU mortality prediction have been presented. Experimental evaluation of the proposed approach in comparison to standard severity scores like SAPS-II, SOFA and OASIS showed that the proposed model outperformed them by a margin of 12–16% in terms of prediction accuracy.
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Notes
- 1.
Medical Information Mart for Intensive Care, available online https://mimic.physionet.org/.
- 2.
International Classification of Diseases, 9th Revision (ICD-9).
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Acknowledgements
We gratefully acknowledge the use of the facilities at the Department of Information Technology, NITK Surathkal, funded by Govt. of India’s DST-SERB Early Career Research Grant (ECR/2017/001056) to the second author.
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Krishnan, G.S., Sowmya Kamath, S. (2019). A Supervised Approach for Patient-Specific ICU Mortality Prediction Using Feature Modeling. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2018. Lecture Notes in Electrical Engineering, vol 542. Springer, Singapore. https://doi.org/10.1007/978-981-13-3648-5_32
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DOI: https://doi.org/10.1007/978-981-13-3648-5_32
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