New machine learning tool predicts devastating intestinal disease in premature infants

Researchers from Columbia Engineering and the University of Pittsburgh have developed a sensitive and specific early warning system for predicting necrotizing enterocolitis (NEC) in premature infants before the life-threatening intestinal disease occurs. The prototype predicts NEC accurately and early, using stool microbiome features combined with clinical and demographic information. ‘The lessons we’ve learned from our new technique could well translate to other genetic or proteomic datasets and inspire new machine learning algorithms for healthcare datasets.’