Improving sepsis prediction in intensive care with SepsisAI: A clinical decision support system with a focus on minimizing false alarms.

ABSTRACT: Prediction of sepsis using machine-learning approaches has recently gained traction. However, the lack of translation of these algorithms into clinical routine remains a major issue. Existing early sepsis detection methods are either based on the older definition of sepsis or do not accurately detect sepsis leading to the high frequency of false-positive alarms. This results in a well-known issue of clinicians' "alarm fatigue", leading to decreased responsiveness and identification, ultimately resulting in delayed clinical intervention. Hence, there is a fundamental, unmet need for a clinical decision system capable of accurate and timely sepsis diagnosis, running at the point of need. In this work, SepsisAI-a deep-learning algorithm based on long short-term memory (LSTM) networks was deve

SUBMITTER: Gupta A 

PROVIDER: S-EPMC11318852 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature