Automated Prioritization of Sick Newborns for Whole Genome Sequencing Using Clinical Natural Language Processing and Machine Learning

Background Rapidly and efficiently identifying critically ill infants for WGS is a costly and challenging task currently performed by scarce, highly trained experts, and is a major bottleneck for application of WGS in the NICU. Automated means to prioritize patients for WGS are thus badly needed. Methods Institutional databases of Electronic Health Records (EHRs) are logical starting points for identifying patients with undiagnosed Mendelian diseases. We have developed automated means to prioritize patients for Rapid and Whole Genome Sequencing (rWGS and WGS) directly from clinical notes. Our approach combines a Clinical Natural Language Processing (CNLP) workflow with a machine learning-based prioritization tool we call the Mendelian Phenotype Search Engine (MPSE). Results MPSE accurately and robustly identified NICU patients selected for WGS by clinical experts from Rady Children’s Hospital in San Diego (AUC 0.86) and the University of Utah (AUC 0.85). In addition to effectively identifying patients for WGS, MPSE scores also strongly prioritize diagnostic cases over non-diagnostic cases, with projected diagnostic yields exceeding 50% throughout the first and second quartiles of score-ranked patients. Conclusions Our results indicate that an entirely automated pipeline for selecting acutely ill infants in neonatal intensive care units (NICU) for WGS can meet or exceed diagnostic yields obtained through current selection procedures, which require time-consuming manual review of clinical notes and histories by specialized personnel.