Human-machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system
https://pubmed.ncbi.nlm.nih.gov/35864312/
A Reinforcement Learning Application for Optimal Fluid and Vasopressor Interventions in Septic ICU Patients
https://pubmed.ncbi.nlm.nih.gov/36086153/
Detecting sepsis from photoplethysmography: strategies for dataset preparation
https://pubmed.ncbi.nlm.nih.gov/36086115/
Performance of presepsin and procalcitonin predicting culture-proven bacterial infection and 28-day mortality: A cross sectional study
https://pubmed.ncbi.nlm.nih.gov/36072944/
Hepcidin discriminates sepsis from other critical illness at admission to intensive care
https://pubmed.ncbi.nlm.nih.gov/36050405/
Red blood cell distribution width as prognostic factor in sepsis: A new use for a classical parameter
https://pubmed.ncbi.nlm.nih.gov/35667275/
Comment: https://pubmed.ncbi.nlm.nih.gov/35989246/
Proinflammatory cytokines levels in sepsis and healthy volunteers, and tumor necrosis factor-alpha associated sepsis mortality: A systematic review and meta-analysis
https://pubmed.ncbi.nlm.nih.gov/36044827/
Heparin-binding protein-enhanced quick SOFA score improves mortality prediction in sepsis patients
https://pubmed.ncbi.nlm.nih.gov/36035420/
Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing
https://pubmed.ncbi.nlm.nih.gov/35864251/
Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis
https://pubmed.ncbi.nlm.nih.gov/35864252/
Enhanced Bedside Mortality Prediction Combining Point of Care Lactate and the Quick Sequential Organ Failure Assessment (QSOFA) Score in Patients Hospitalised With Suspected Infection in Southeast Asia: A Cohort Study
https://pubmed.ncbi.nlm.nih.gov/35961351/
Comment: “A combined qSOFA-lactate model for sepsis-related mortality prediction: a small step towards an elusive answer?” https://pubmed.ncbi.nlm.nih.gov/35961331/