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Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques

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Abstract

This study sought to determine a mortality prediction model that could be used for triage in the setting of acute hemorrhage from trauma. To achieve this aim, various machine learning techniques were applied using the rat model in acute hemorrhage. Thirty-six anesthetized rats were randomized into three groups according to the volume of controlled blood loss. Measurements included heart rate (HR), systolic and diastolic blood pressures (SBP and DBP), mean arterial pressure, pulse pressure, respiratory rate, temperature, blood lactate concentration (LC), peripheral perfusion (PP), shock index (SI, SI = HR/SBP), and a new hemorrhage-induced severity index (NI, NI = LC/PP). NI was suggested as one of the good candidates for mortality prediction variable in our previous study. We constructed mortality prediction models with logistic regression (LR), artificial neural networks (ANN), random forest (RF), and support vector machines (SVM) with variable selection. The SVM model showed better sensitivity (1.000) and area under curve (0.972) than the LR, ANN, and RF models for mortality prediction. The important variables selected by the SVM were NI and LC. The SVM model may be very helpful to first responders who need to make accurate triage decisions and rapidly treat hemorrhagic patients in cases of trauma.

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Acknowledgments

This study was supported by a faculty research Grant from Yonsei University College of Medicine for 2011 (6-2011-0087).

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Authors and Affiliations

  1. Department of Biomedical Engineering, Chungbuk National University, College of Medicine, Cheongju, Korea

    Kyung-Ah Kim

  2. Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Korea

    Joon Yul Choi, Sung Kean Kim & Deok Won Kim

  3. Department of Medicine, Yonsei University, Seoul, Korea

    Tae Keun Yoo

  4. Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Korea

    Sung Kean Kim & Deok Won Kim

  5. Department of Electrical System, DongYang Mirae University, Seoul, Korea

    KilSoo Chung

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  1. Kyung-Ah Kim
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Correspondence to Deok Won Kim.

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Kim, KA., Choi, J.Y., Yoo, T.K. et al. Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. Med Biol Eng Comput 51, 1059–1067 (2013). https://doi.org/10.1007/s11517-013-1091-0

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  • DOI: https://doi.org/10.1007/s11517-013-1091-0

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