G Barosi, L Magnani, M Stefanelli
Medical diagnostic reasoning: epistemological modeling as a strategy for design of computer-based consultation programs
Theoretical medicine
perinatal arterial ischemic stroke pais focal vascular brain injury presumed occur fetal period first 28 days life leading cause hemiparetic cerebral palsy multiple maternal intrapartum delivery fetal factors associated pais studies limited modest sample sizes complex interactions factors machine learning approaches use large complex data sets enable unbiased identification clinical predictors yet applied pais combined large pais data sets used machine learning methods identify clinical pais factors compare datadriven approach previously described literaturedriven clinical prediction models common data elements 3 registries patients pais alberta perinatal stroke project canadian cerebral palsy registry international pediatric stroke study longitudinal cohort healthy controls alberta pregnancy outcomes nutrition study used identify potential predictors pais inclusion criteria term birth idiopathic pais absence primary causative medical condition data including maternalpregnancy intrapartum neonatal factors collected january 2003 march 2020 common data elements entered validated random forest machine learning pipeline identify highest predictive features develop predictive model univariable analyses completed post hoc assess relationship predictor outcome machine learning model developed using data 2571 neonates including 527 cases 20 2044 controls 80 mean 21 features selected random forest machine learning approach predicted outcome approximately 865 balanced accuracy factors selected priori literaturedriven variable selection also identified important machine learning model maternal age recreational substance exposure tobacco exposure intrapartum maternal fever low apgar score 5 minutes additional variables identified machine learning included utero alcohol exposure infertility miscarriage primigravida meconium spontaneous vaginal delivery neonatal head circumference 1minute apgar score overall machine learning model performed better area curve auc 093 literaturedriven model auc 073 machine learning may alternative unbiased method identify clinical predictors associated pais identification previously suggested novel clinical factors requires cautious interpretation supports multifactorial nature pais pathophysiology results suggest identification neonates risk pais possible