000109608 001__ 109608
000109608 005__ 20240319080946.0
000109608 0247_ $$2doi$$a10.3390/e24010068
000109608 0248_ $$2sideral$$a125594
000109608 037__ $$aART-2022-125594
000109608 041__ $$aeng
000109608 100__ $$0(orcid)0000-0001-7995-6969$$aEsteban-Escaño, Javier
000109608 245__ $$aMachine learning algorithm to predict acidemia using electronic fetal monitoring recording parameters
000109608 260__ $$c2022
000109608 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109608 5203_ $$aBackground: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.
000109608 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00$$9info:eu-repo/grantAgreement/ES/DGA/E46-20R
000109608 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109608 590__ $$a2.7$$b2022
000109608 592__ $$a0.541$$b2022
000109608 591__ $$aPHYSICS, MULTIDISCIPLINARY$$b40 / 85 = 0.471$$c2022$$dQ2$$eT2
000109608 593__ $$aElectrical and Electronic Engineering$$c2022$$dQ2
000109608 593__ $$aPhysics and Astronomy (miscellaneous)$$c2022$$dQ2
000109608 593__ $$aMathematical Physics$$c2022$$dQ2
000109608 593__ $$aInformation Systems$$c2022$$dQ2
000109608 594__ $$a4.7$$b2022
000109608 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109608 700__ $$aCastán, Berta
000109608 700__ $$0(orcid)0000-0002-9048-121X$$aCastán, Sergio$$uUniversidad de Zaragoza
000109608 700__ $$aChóliz-Ezquerro, Marta
000109608 700__ $$0(orcid)0000-0002-7538-1501$$aAsensio, César
000109608 700__ $$0(orcid)0000-0002-9496-9714$$aLaliena, Antonio R.
000109608 700__ $$0(orcid)0009-0001-6297-2767$$aSanz-Enguita, Gerardo
000109608 700__ $$0(orcid)0000-0002-6474-2252$$aSanz, Gerardo$$uUniversidad de Zaragoza
000109608 700__ $$0(orcid)0000-0002-3007-302X$$aEsteban, Luis Mariano
000109608 700__ $$aSavirón, Ricardo
000109608 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000109608 7102_ $$11013$$2645$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Obstetricia y Ginecología
000109608 773__ $$g24, 1 (2022), 68 [16 pp.]$$pEntropy$$tENTROPY$$x1099-4300
000109608 8564_ $$s3917069$$uhttps://zaguan.unizar.es/record/109608/files/texto_completo.pdf$$yVersión publicada
000109608 8564_ $$s2719985$$uhttps://zaguan.unizar.es/record/109608/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109608 909CO $$ooai:zaguan.unizar.es:109608$$particulos$$pdriver
000109608 951__ $$a2024-03-18-12:39:11
000109608 980__ $$aARTICLE