Pediatric sepsis increasingly screenable by AI

"Screening for sepsis in children and babies has grown quickly over the past several years. As methods and approaches multiply, machine learning continues looking like an eventual first-line diagnostic option.

The forecast is put forth, albeit indirectly, by a pair of pediatric emergency medicine specialists at Harvard and the University of Pennsylvania in a narrative review of the relevant medical literature published Aug. 20 by Pediatric Research.

“Machine learning and artificial intelligence offer the promise of creating better sepsis identification tools that leverage big data and can incorporate elements from the EHR that previously required manual input,” write Matthew Eisenberg, MD, MPH, of Boston Children’s Hospital and Fran Balamuth, MD, PhD, of Children’s Hospital of Philadelphia. “Rather than utilize rule-based thresholds such as systemic inflammatory response syndrome (SIRS), the pediatric early warning score, or the sequential organ failure assessment, such algorithms are trained to run complex tasks on large amounts of data in order to predict adverse outcomes.”

Sepsis, which occurs when the immune system turns on itself following an infection, is notoriously difficult to diagnose early in pediatrics. That’s because elevated temperature, rapid heartbeat and fast breathing—the early symptoms of sepsis—are common with many pediatric sicknesses..." Lire la suite