Sonia Verdugo-Castro, Ma Cruz Sánchez-Gómez, Alicia García-Holgado
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
Heliyon
electronic fetal monitoring used us hospital births significant limitations achieving intended goal preventing intrapartum hypoxicischemic injury novel deep learning techniques improve complex data processing pattern recognition medicine study aimed apply deep learning approaches develop validate model predict fetal acidemia electronic fetal monitoring data database created using intrapartum electronic fetal monitoring data 2006 2020 large multisite academic health system data divided training testing sets equal distribution acidemic cases several different deep learning architectures explored primary outcome umbilical artery acidemia investigated 4 clinically meaningful thresholds 720 715 710 705 along base excess receiver operating characteristic curves generated area receiver operating characteristic assessed determine performance models external validation performed using publicly available czech database electronic fetal monitoring data total 124777 electronic fetal monitoring files available 77132 30 missingness last 60 minutes electronic fetal monitoring tracing 21041 matched corresponding umbilical cord gas result 10182 timestamped within 30 minutes last electronic fetal monitoring reading composed final dataset prevalence rates outcomes data 209 ph 72 91 ph 715 33 ph 710 13 ph 705 best performing model achieved area receiver operating characteristic 085 ph threshold 705 predicting joint outcome ph 705 base excess less 10 meql area receiver operating characteristic 089 achieved predicting ph 720 base excess less 10 meql area receiver operating characteristic 087 achieved ph 715 positive predictive value 30 model achieved sensitivity 90 specificity 48 application deep learning methods intrapartum electronic fetal monitoring analysis achieves promising performance predicting fetal acidemia technology could help improve accuracy consistency electronic fetal monitoring interpretation