Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data.

ABSTRACT: Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a no

SUBMITTER: Feng J 

PROVIDER: S-EPMC8280207 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature