Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation.
Journal:
BMC medical informatics and decision making
Published Date:
Oct 22, 2021
Abstract
PURPOSE: Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation.