Arnav Gupta
Early detection of sepsis is crucial for timely intervention and improved patient outcomes. Traditional diagnostic methods often fail to identify sepsis in its initial stages, leading to delays in treatment. This study investigates the potential of machine learning techniques, particularly multilayer perceptron (MLP) models, for early sepsis prediction using physiological data from intensive care unit (ICU) patients. A large dataset comprising approximately 40,000 ICU patient records from two hospital systems was analyzed. Various machine learning algorithms were implemented and compared, with a focus on optimizing an MLP model for early sepsis detection. The MLP model was trained on physiological data collected up to six hours before the clinical manifestation of sepsis symptoms. The findings demonstrated that the MLP model outperformed traditional methods, accurately predicting sepsis onset up to six hours before clinical symptoms became apparent. The MLP model exhibited significant improvements in accuracy compared to conventional models used for sepsis diagnosis. The ability to predict sepsis development at an early stage holds immense clinical value. Early detection facilitated by the MLP model can potentially lead to prompt administration of appropriate treatments, thereby improving patient outcomes and reducing mortality rates associated with sepsis. Moreover, early identification of sepsis cases can optimize resource allocation and improve operational efficiency in critical care environments. This study highlights the efficacy of MLP models in leveraging physiological data for early sepsis prediction. The proposed approach offers a promising solution for enhancing sepsis management protocols, ultimately contributing to improved patient care and resource utilization in intensive care settings.