The impact of recency and adequacy of historical information on sepsis predictions using machine learning.

ABSTRACT: Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis. This study contributes to this literature by providing clear insights regarding the role of the recency and adequacy of historical information in predicting sepsis using ML. To this end, we implemented a deep learning model using a bidirectional long short-term memory (BiLSTM) algorithm and compared it with six other ML algorithms based on numerous combinations of the prediction horizons (to capture information recency

SUBMITTER: Zargoush M 

PROVIDER: S-EPMC8531301 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature