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Early Prediction of Sepsis Utilizing Machine Learning Models

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Evolution in Computational Intelligence (FICTA 2023)

Abstract

Sepsis is an usually dangerous disease caused by the body’s response to an infection, which usually result in tissue damage, organ failure, or death. Inflammation spreads throughout the body as a result of the immune system’s response to the infection, which can lead to organ damage and failure. Fever, increased heart rate, low blood pressure, and confusion or disorientation are all symptoms of sepsis. Early recognition and treatment with antibiotics and supportive care can improve outcomes, but sepsis can progress rapidly, so prompt medical attention is crucial. The early detection of sepsis is critical for improving patient outcomes, as the condition can progress rapidly and become life-threatening. Traditional methods for sepsis detection can be subjective and may not always accurately identify the condition in its early stages. To address this issue, machine learning models have been proposed as a tool for early sepsis detection. In the proposed work, several machine learning models were applied to patient data, including vital signs, lab results, and electronic health records, to identify patterns and trends that might not be apparent to human clinicians. The performance of these models was evaluated using a dataset of real-world patient data (https://my.clevelandclinic.org/health/diseases/23255-septic-shock) and compared to traditional sepsis detection methods. The results showed that the machine learning models were able to accurately predict sepsis with high sensitivity and specificity, providing a promising solution for early sepsis detection in clinical settings. This work highlights the potential of machine learning models for improving sepsis detection and management and provides a basis for further research in this area.

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Correspondence to J. Avanija .

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Sasi Kiran, J., Avanija, J., Reddy, A.R., Naga Rama Devi, G., Charan, N.S., Fatima, T. (2023). Early Prediction of Sepsis Utilizing Machine Learning Models. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_27

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