International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 08 | Aug 2024
p-ISSN: 2395-0072
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Detection of Septic Condition based on patient Laboratory Values using XGBoost Model Komal Kumari Roy1, Prof. Preeti Rai2 1Reseacrh Scholar, Department of CSE, Gyan Ganga Institute of Technology and sciences, Jabalpur, M.P. 2Professor, Departmet of CSE, Gyan Ganga Institute of Technology and Sciences, Jabalpur, M.P.
---------------------------------------------------------------------***-------------------------------------------------------------------Furthermore, SIRS criteria have been criticized for Abstract –Sepsis is a complex medical condition that can
inadequate specificity and sensitivity since SIRS may occur in several non-infectious scenarios [8]. The SOFA score is based on the degree of dysfunction of six organ systems, such as respiratory, coagulation, hepatic, cardiovascular, renal, and neurological systems [9]. According to the Sepsis3 guidelines, patients with a SOFA score of 2 or more are associated with an organ failure consequent to the infection, meaning that a higher SOFA score indicates the increased mortality risk. The Modified Early Warning Score (MEWS) is another scoring system used for the determination or prediction of sepsis [10]. These updated definitions and gold standards have been adapted to facilitate the earlier identification and timely management of septic patients. However, sepsis is a dynamic condition and, hence, such criteria may not yield accurate outcomes. Consequently, early prediction of the onset of sepsis remains a challenging problem. There has been a significant surge in using deep neural networks (DNNs) and machine learning for solving multivariate, complex, and nonlinear problems. Training such networks requires a significant volume of data. Meanwhile, the intensive care unit (ICU) patients are monitored consistently. This has generated an abundance of data, which allows for training ML and DNNs for event prediction or decision support in critical care cases [11].
manifest in various ways and progress through different stages. Clinicians often categorize sepsis into different types or stages based on the severity and specific clinical characteristics. This paper review research that have already done by various researchers in the field as well as introduced a novel prediction model using XGBoost for septic patient’s detection based on medical data. The research began by addressing the challenges of missing data in the available patient records. To counteract the potential negative impact of this missing information on prediction accuracy, a unique preprocessing and early prediction network is proposed. This model not only identified missing data patterns to enhance prediction accuracy but also extended its applicability to broader detection timeframes of the septic conditions of patients. Keywords: Sepsis detection, Machine learning, Random forest, Boosting ensemble, early predictions.
1. INTRODUCTION Sepsis is a life-threatening medical emergency that can rapidly lead to tissue damage, organ failure, and death [1]. Sepsis is considered responsible for more than one-third of the hospital deaths in the United States and the increased incidence have been a growing concern [2]. It is one of the most expensive conditions to treat, representing 13% of the total U.S. healthcare cost. Additionally, statistics show that the average length of stay in hospitals for sepsis patients is nearly 75% longer than that of other medical conditions [3]. It has been reported that the early intervention and recognition of sepsis can significantly reduce the overall mortality and cost burden of sepsis. The importance of early prediction and treatment of sepsis is emphasized in the current clinical and observational studies that show a lower risk of mortality for sepsis patients who received antibiotics and intravenous fluids on time [4], [5]. In another study, it is reported that hourly delays in the initiation of antibiotic therapy can cause an average increase in the mortality rate by 7.6% [6]. In the context of sepsis diagnosis, the Systemic Inflammatory Response Syndrome (SIRS) criteria were considered to be central [7]. Recently, the third international consensus definition for sepsis and septic shock (Sepsis-3) was published. For diagnostic criterion, the Sequential Organ Failure Assessment (SOFA) scoring system was proposed.
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Recent studies have incorporated such ML and DNN-based approaches using electronic health records (EHRs) for identifying the early stages of complex diseases [12], [13], [14]. In sepsis cases, attention has been placed on creating an accurate and swift prediction model as an extension of the clinical decision since the performance of the deep learning models has been found significantly higher than traditional scoring systems. In [15] and [16], authors have been systematically reviewed and evaluated studies employing machine learning for the prediction of sepsis in the ICU. Our strategy is to build a ML-based preprocessing block that has the ability to learn the underlying dependencies and correlations of the observed time series to estimate the missing values.
2. Related Work Early detection and diagnosis of sepsis can increase a patient's chances of survival and improve long-term health. In this paper [17] we use shapley additive explanations
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