LiSep LSTM: A Machine Learning Algorithm for Early Detection of Septic Shock.

ABSTRACT: Sepsis is a major health concern with global estimates of 31.5 million cases per year. Case fatality rates are still unacceptably high, and early detection and treatment is vital since it significantly reduces mortality rates for this condition. Appropriately designed automated detection tools have the potential to reduce the morbidity and mortality of sepsis by providing early and accurate identification of patients who are at risk of developing sepsis. In this paper, we present "LiSep LSTM"; a Long Short-Term Memory neural network designed for early identification of septic shock. LSTM networks are typically well-suited for detecting long-term dependencies in time series data. LiSep LSTM was developed using the machine learning framework Keras with a Google TensorFlow back end. The model

SUBMITTER: Fagerstrom J 

PROVIDER: S-EPMC6805937 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature