Variational Autoencoder-Based Framework for Improved Sepsis Detection in ICU Patients

Sepsis remains one of the primary morbidity and mortality causes in the intensive care units (ICUs).However, given that it is set to be discovered early one could have better outcomes among the patients. The traditional practices concerning the application of machine learning to predict sepsis are in trouble. The data sets are often skewed, lack items, and fail to capture in a complete sense the complex patterns that are present in ICU data. The following is a plan to develop a sepsis prediction framework based on Variational Autoencoders (VAEs).VAEs decrease the size of clinical data and are also able to generate similar records. Therefore, this methodology helps to address the issue of the lack of data and an unequal number of classes. One available to us were the records generated by us along with the actual ICU data, which was used to train a machine learning model. including, among others, Random Forest. Our analysis shows that using VAE-generated data improved quantitative measures like accuracy, precision, recall, F1-score, and confusion-matrix analysis. This method improves sepsis prediction and shows that generative techniques could be beneficial for other critical health applications. This study presents a novel approach for generating detailed predictive analytics in ICU settings using generative models.