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Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning

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Abstract

Sepsis is a life-threatening disease that is associated with organ dysfunction. It occurs due to the body’s dysregulated response to infection. It is difficult to identify sepsis in its early stages, this delay in identification has a dramatic effect on mortality rate. Developing prognostic tools for sepsis prediction has been the focus of various studies over previous decades. However, most of these studies relied on tracking a limited number of features, as such, these approaches may not predict sepsis sufficiently accurately in many cases. Therefore, in this study, we concentrate on building a more accurate and medically relevant predictive model for identifying sepsis. First, both NSGA-II (a multi-objective genetic algorithm optimization approach) and artificial neural networks are used concurrently to extract the optimal feature subset from patient data. In the next stage, a deep learning model is built based on the selected optimal feature set. The proposed model has two layers. The first is a deep learning classification model used to predict sepsis. This is a stacking ensemble of neural network models that predicts which patients will develop sepsis. For patients who were predicted to have sepsis, data from their first six hours after admission to the ICU are retrieved, this data is then used for further model optimization. Optimization based on this small, recent timeframe leads to an increase in the effectiveness of our classification model compared to other models from previous works. In the second layer of our model, a multitask regression deep learning model is used to identify the onset time of sepsis and the blood pressure at that time in patients that were predicted to have sepsis by the first layer. Our study was performed using the medical information from the intensive care MIMIC III real-world dataset. The proposed classification model achieved 0.913, 0.921, 0.832, 0.906 for accuracy, specificity, sensitivity, and AUC, respectively. In addition, the multitask regression model obtained an RMSE of 10.26 and 9.22 for predicting the onset time of sepsis and the blood pressure at that time, respectively. There are no other studies in the literature that can accurately predict the status of sepsis in terms of its onset time and predict medically verifiable quantities like blood pressure to build confidence in the onset time prediction. The proposed model is medically intuitive and achieves superior performance when compared to all other current state-of-the-art approaches.

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Funding

This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Creative Consilience Program(IITP-2021-2020-0-01821) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation), and the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1A2C1011198).

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Authors and Affiliations

  1. Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh, 13518, Egypt

    Nora El-Rashidy

  2. College of Computing and Informatics, Sungkyunkwan University, Suwon, South Korea

    Tamer Abuhmed

  3. Computer Science Department, College of Computer Science and Information System, Umm Al-Qura University, Mecca, Saudi Arabia

    Louai Alarabi

  4. Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, 13518, Egypt

    Hazem M. El-Bakry & Samir Abdelrazek

  5. Department of Software, Sejong University, Seoul, South Korea

    Farman Ali

  6. Faculty of Computer Science and Engineering, Galala University, 435611, Suez, Egypt

    Shaker El-Sappagh

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  1. Nora El-Rashidy
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Appendices

Appendix 1

See Tables 10, 11.

Table 10 List of abbreviations
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Table 11 Features used for Sepsis
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Appendix 2

See Tables 12, 13, 14.

Table 12 Sepsis-related organ failure assessment scoring system
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Table 13 qSOFA Score
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Table 14 Modified Early Warning Score
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Appendix 3

See Table 15.

Table 15 Hyperparameters for NSGA-II, ensemble model, regular classifiers, regular regressors, and deep learning regressor
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El-Rashidy, N., Abuhmed, T., Alarabi, L. et al. Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning. Neural Comput & Applic 34, 3603–3632 (2022). https://doi.org/10.1007/s00521-021-06631-1

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