Artificial Intelligence Occult Sepsis Detection in the Emergency Department: A Large, Multicenter Real-World Data Study
Annals of Emergency Medicine, 78 (4), S24, 2021. Plenary session abstract presented at ACEP21.
In collaboration with one of the global leaders in acute care biomarkers, Dr. Frank Peacock, we have demonstrated the ability to go beyond traditional alerts and showed how our AI can detect sepsis many hours before it satisfies traditional clinical criteria ("occult sepsis").
Read the abstract
Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study
JMIR 2020;22(12):e24048
In this study that included a large number of sites and patient encounters, we describe a model that can discriminate COVID-19 using only routine blood tests, and that generalizes well to hospitals other than the ones it was trained on.
Read the paper
Other papers by our team
- WF Peacock et al: "Procalcitonin as a biomarker for early sepsis in the emergency department." European Journal of Emergency Medicine 21.2 (2014): 112-117.
- WF Peacock et al: "Cardiac troponin and outcome in acute heart failure." New England Journal of Medicine 358.20 (2008): 2117-2126.
- J Tamayo-Sarver et al: "Predicting emergency department “bouncebacks”: a retrospective cohort analysis." Western Journal of Emergency Medicine 20.6 (2019): 865.
.svg)
.svg)
