109608 20240319080946.0 doi 10.3390/e24010068 sideral 125594 ART-2022-125594 eng Esteban-Escaño, Javier (orcid)0000-0001-7995-6969 Machine learning algorithm to predict acidemia using electronic fetal monitoring recording parameters 2022 Access copy available to the general public Unrestricted Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections. info:eu-repo/grantAgreement/ES/DGA/E46-20R info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.7 2022 PHYSICS, MULTIDISCIPLINARY 40 / 85 = 0.471 2022 Q2 T2 0.541 2022 Electrical and Electronic Engineering 2022 Q2 Physics and Astronomy (miscellaneous) 2022 Q2 Mathematical Physics 2022 Q2 Information Systems 2022 Q2 4.7 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Castán, Berta Castán, Sergio Universidad de Zaragoza (orcid)0000-0002-9048-121X Chóliz-Ezquerro, Marta Asensio, César (orcid)0000-0002-7538-1501 Laliena, Antonio R. (orcid)0000-0002-9496-9714 Sanz-Enguita, Gerardo (orcid)0009-0001-6297-2767 Sanz, Gerardo Universidad de Zaragoza (orcid)0000-0002-6474-2252 Esteban, Luis Mariano (orcid)0000-0002-3007-302X Savirón, Ricardo 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 1013 645 Universidad de Zaragoza Dpto. Cirugía Área Obstetricia y Ginecología 24, 1 (2022), 68 [16 pp.] Entropy ENTROPY 1099-4300 3917069 http://zaguan.unizar.es/record/109608/files/texto_completo.pdf Versión publicada 2719985 http://zaguan.unizar.es/record/109608/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:109608 articulos driver 2024-03-18-12:39:11 ARTICLE