Reshma Shah, Giovanna Elena Savastano, Monica Fehrenbach, Kruti Acharya
Facilitating coordination between medical and educational systems to improve access to pediatric therapies for preschool children with developmental delays and disabilities
Families, systems & health : the journal of collaborative family healthcare
haemorrhage leading cause preventable death trauma accurately predicting patients blood transfusion requirement essential difficult machine learning ml field artificial intelligence emerging within medicine accurate prediction modelling systematic review aimed identify evaluate ml models predict blood transfusion trauma systematic review registered international prospective register systematic reviews crd4202237110 medline embase cochrane central register controlled trials systematically searched publications reporting ml model predicted blood transfusion injured adult patients included data extraction risk bias assessment performed using validated frameworks data synthesised narratively due significant heterogeneity twentyfive ml models blood transfusion prediction trauma identified models incorporated diverse predictors varied ml methodologies predictive performance variable eight models achieved excellent discrimination auroc 09 nine models achieved good discrimination auroc 08 internal validation two models reported measures calibration four models externally validated prospective cohorts bleeding risk index compensatory reserve index marsden model mina model studies considered high risk bias often due retrospective datasets small sample size lack external validation review identified twentyfive ml models developed predict blood transfusion requirement injury seventeen ml models demonstrated good excellent performance insilico four models externally validated date ml models demonstrate potential early individualised blood transfusion prediction research critically required narrow gap ml model development clinical application systematic review without metaanalysis level iv