A predictive model of central line-association bloodstream infection (CLABSI).
- Specifically designed to improved predictive analysis of patients who are at risk for CLABSI.
- Scored higher predictive values on average than similar predictive modeling without the hypotheses driven approach (Bidirectional LSTM with Focal Loss and Attention Mechanism).
Central line-associated bloodstream infections (CLABSIs) are a major cause of healthcare associated infections among hospitalized children and contribute to increased morbidity, length of hospital stay, and cost. The Center for Disease Control estimates 80,000 new CLABSIs occur in the U.S. every year and increase the risk of mortality by 12%-15%. Early detection of CLABSIs can prevent adverse outcomes, reduce costs, and improve quality of care. Researchers at Emory University have developed a predictive model to predict the onset of the infection prior to clinical suspicion. This model is associated with the global market for healthcare acquired infections, which should grow from $24 billion in 2021 to $36.2 billion by 2026 at a CAGR of 8.6% from 2021-2026 (BCC Research HLC092E).
Researchers have developed a predictive model to predict the onset of central line-associate bloodstream infection (CLABSI) in children with a central during the next 48 hours of their hospitalization utilizing electric health record data. Researchers found that Temperature and Platelet count were the 2 largest factors indicating the onset of CLABSI in a juvenile patient. The researchers’ model combined the factors of Bidirectional LSTM with Focal Loss and Attention Mechanism outperformed the other model examples in every category except for Sensitivity and Negative Predicative Value indicating the model is capable of being implemented in a real-time setting and serve as a clinical support system.
TTO Home Page: https://emoryott.technologypublisher.com
Name: David Mudd
Title: Licensing Associate