A personalized risk stratification tool for perinatal morbidity and mortality using explainable artificial intelligence (AI)

Late preterm delivery is frequently considered when competing maternal risks begin to outweigh perinatal risks, but tools for precise, personalized perinatal risk estimation are lacking. Traditional regression methods do not capture the complex interactions specific to each possible combination of risk factors, which can be synergistic rather than simply additive. AI-based Probabilistic Graphical Models (PGMs) can quantify these synergies transparently. We aimed to develop an ‘explainable AI’ machinery for personalized perinatal morbidity risk estimation using variables from clinical and social determinants of health domains.