Craniosynostosis is the premature fusion of 1 cranial sutures and often requires surgical intervention. Surgery may involve extensive osteotomies, which can lead to substantial blood loss. Currently, there are no consensus recommendations for guiding blood conservation or transfusion in this patient population. The aim of this study is to develop a machine-learning model to predict blood product transfusion requirements for individual pediatric patients undergoing craniofacial surgery. Using data from 2143 patients in the Pediatric Craniofacial Surgery Perioperative Registry, we assessed 6 machine-learning classification and regression models based on random forest, adaptive boosting (AdaBoost), neural network, gradient boosting machine (GBM), support vector machine, and elastic net methods with inputs from 22 demographic and preoperative features.

A study published in JAMA Psychiatry outlined sex-specific suicide prediction models by using a novel machine learning design. Lead study author Jaimie L. Gradus, DMSc, DSc, of Boston University School of Public Health, Massachusetts, and colleagues used machine learning to analyze data from Danish single-payer healthcare and social registries from 1995 through 2015. As such, the source population for the case cohort study comprised all people living in Denmark since 1995. The main outcome was death from suicide, and the study included 1339 variables as exposures. The researchers created a comparison sub-cohort comprised of a 5% random sample of registry data.