Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease of prematurity. Characterized by sudden and progressive intestinal inflammation and tissue death, it affects up to 11,000 premature infants in the United States annually, and 15-30% of affected babies die from NEC. Survivors often face long-term intestinal and neurodevelopmental complications. Researchers from Columbia Engineering and the University of Pittsburgh have developed a sensitive and specific early warning system for predicting NEC in premature infants before the disease occurs. The prototype predicts NEC accurately and early, using stool microbiome features combined with clinical and demographic information. The pilot study was presented virtually on July 23 at ACM CHIL 2020.

Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease of prematurity. Characterized by sudden and progressive intestinal inflammation and tissue death, it affects up to 11,000 premature infants in the United States annually, and 15-30 percent of affected babies die from NEC. Survivors often face long-term intestinal and neurodevelopmental complications. Researchers from Columbia Engineering and the University of Pittsburgh have developed a sensitive and specific early warning system for predicting NEC in premature infants before the disease occurs. The prototype predicts NEC accurately and early, using stool microbiome features combined with clinical and demographic information. The pilot study was presented virtually on July 23 at ACM CHIL 2020.

Almost 400,000 babies were born prematurely--before 37 weeks gestation--in 2018 in the United States. One of the leading causes of newborn deaths and long-term disabilities, preterm birth (PTB) is considered a public health problem with deep emotional and challenging financial consequences to families and society. If doctors were able to use data and artificial intelligence (AI) to predict which pregnant women might be at risk, many of these premature births might be avoided. "Premature birth prediction has been an exceedingly challenging problem," said Ansaf Salleb-Aouissi, a senior lecturer in discipline from the computer science department. "But we are now at a point where we can use machine learning to develop a dynamic risk prediction system for pregnant women. Creating a system that can process large models of data with AI algorithms we develop would be a great benefit to supplement physicians' 'real-life' expertise."