Early Sepsis Prediction in Intensive Care Patients using Random Forest Classifier - Shenoy, Aparna; Viswanatha, KV; Gondkar, Raju Ramakrishna (International Journal of Computer Sciences and Engineering, 2020)
Sepsis is one of the most common causes of morbidity and mortality in the Intensive Care Unit (ICU) patients. The lack of sensitive and specific clinical and laboratory variables for early identification of sepsis in critically ill patients is the causative factor for needless and delayed or untimely interruption of a proper antibiotic therapy. The current work developed a machine learning-based early sepsis prediction model in intensive care patients with vital parameters which evaluated the goodness of model fit and its accuracy. The predictors were extracted from combinations of vital sign measure and their changes over time. The dataset consisted of 20,336 patients (medical and surgical) who were admitted in ICU. Random Forest Classifier was used as the Machine learning algorithm for developing a predictive model. For the early prediction of sepsis in ICU patients, the Random Forest Classifier achieved an AUROC curve of 0.58 for the data collected from the patients within 24 hours. Sepsis is being the common cause of admission in ICU worldwide, a machine learning technique adopting statistical methods to conclude relationships between patient features and outcomes in large data set was successfully applied to predict adverse events.