{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:17:29Z","timestamp":1791893849202,"version":"4.3.4"},"reference-count":67,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":128,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"publisher","award":["EP\/V519935\/1"],"award-info":[{"award-number":["EP\/V519935\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012317","name":"UCLH Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012317","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Current Research in Physiology"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1016\/j.crphys.2023.100099","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T04:00:49Z","timestamp":1684468849000},"page":"100099","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":41,"special_numbering":"C","title":["Machine learning and disease prediction in obstetrics"],"prefix":"10.1016","volume":"6","author":[{"given":"Zara","family":"Arain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stamatina","family":"Iliodromiti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Slabaugh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna L.","family":"David","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4167-7782","authenticated-orcid":false,"given":"Tina T.","family":"Chowdhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.crphys.2023.100099_bib1","first-page":"1","article-title":"A comprehensive survey on signal processing and machine learning techniques for non-invasive fetal ECG extraction","author":"Abel","year":"2022","journal-title":"Multimedia Tools Appl."},{"issue":"2","key":"10.1016\/j.crphys.2023.100099_bib2","doi-asserted-by":"crossref","first-page":"113","DOI":"10.5468\/ogs.21234","article-title":"Artificial intelligence in obstetrics","volume":"65","author":"Ahn","year":"2022","journal-title":"Obstetr. Gynecol. Sci."},{"issue":"2","key":"10.1016\/j.crphys.2023.100099_bib3","article-title":"Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour","author":"Alfirevic","year":"2017","journal-title":"Cochrane Database Syst. Rev."},{"key":"10.1016\/j.crphys.2023.100099_bib4","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.placenta.2020.10.015","article-title":"Maternal thyroid profile in first and second trimester of pregnancy is correlated with gestational diabetes mellitus through machine learning","volume":"103","author":"Araya","year":"2021","journal-title":"Placenta"},{"issue":"5","key":"10.1016\/j.crphys.2023.100099_bib5","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1038\/s41591-021-01342-5","article-title":"An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease","volume":"27","author":"Arnaout","year":"2021","journal-title":"Nat. Med."},{"issue":"1","key":"10.1016\/j.crphys.2023.100099_bib6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12884-022-04631-0","article-title":"Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients","volume":"22","author":"Arnolds","year":"2022","journal-title":"BMC Pregnancy Childbirth"},{"issue":"1","key":"10.1016\/j.crphys.2023.100099_bib7","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/s41591-019-0724-8","article-title":"Prediction of gestational diabetes based on nationwide electronic health records","volume":"26","author":"Artzi","year":"2020","journal-title":"Nat. Med."},{"key":"10.1016\/j.crphys.2023.100099_bib8","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1093\/ehjdh\/ztac025","article-title":"Applications of artificial intelligence and machine learning in heart failure","volume":"3","author":"Averbuch","year":"2022","journal-title":"Eur. Heart J. Digit. Health"},{"issue":"1","key":"10.1016\/j.crphys.2023.100099_bib9","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1002\/uog.20168","article-title":"Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix","volume":"54","author":"Bahado\u2010Singh","year":"2019","journal-title":"Ultrasound Obstet. Gynecol."},{"issue":"5","key":"10.1016\/j.crphys.2023.100099_bib10","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1002\/uog.17525","article-title":"Quantitative analysis of cervical texture by ultrasound in mid\u2010pregnancy and association with spontaneous preterm birth","volume":"51","author":"Ba\u00f1os","year":"2018","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10.1016\/j.crphys.2023.100099_bib11","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.3389\/fbioe.2021.780389","article-title":"Using Machine learning to predict complications in pregnancy: a systematic review","volume":"9","author":"Bertini","year":"2022","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"2","key":"10.1016\/j.crphys.2023.100099_bib12","doi-asserted-by":"crossref","first-page":"e49","DOI":"10.1097\/AOG.0000000000002501","article-title":"ACOG practice bulletin no. 190: gestational diabetes mellitus","volume":"131","author":"Bulletins-Obstetrics","year":"2018","journal-title":"Obstet. Gynecol."},{"key":"10.1016\/j.crphys.2023.100099_bib13","first-page":"844","article-title":"Classification of Cardiotocography based on Apriori algorithm and multi-model ensemble classifier","author":"Chen","year":"2022","journal-title":"Front. Cell Dev. Biol."},{"key":"10.1016\/j.crphys.2023.100099_bib14","first-page":"1431","article-title":"Artificial intelligence in obstetric ultrasound: an update and future applications","author":"Chen","year":"2021","journal-title":"Front. Med."},{"issue":"12","key":"10.1016\/j.crphys.2023.100099_bib15","first-page":"3728","article-title":"Novel multichannel entropy features and machine learning for early assessment of pregnancy progression using electrohysterography","volume":"69","author":"Cheng","year":"2022","journal-title":"IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng."},{"key":"10.1016\/j.crphys.2023.100099_bib16","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.1007\/s00192-021-04752-8","article-title":"Prediction model for obstetric anal sphincter injury using machine learning","volume":"32","author":"Chill","year":"2021","journal-title":"Int. Urogynecol. J."},{"issue":"7","key":"10.1016\/j.crphys.2023.100099_bib17","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2020-048008","article-title":"Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence","volume":"11","author":"Collins","year":"2021","journal-title":"BMJ Open"},{"key":"10.1016\/j.crphys.2023.100099_bib18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-019-0079-z","article-title":"Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models","volume":"7","author":"C\u00f6mert","year":"2019","journal-title":"Health Inf. Sci. Syst."},{"issue":"5","key":"10.1016\/j.crphys.2023.100099_bib19","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1136\/heartjnl-2019-316033","article-title":"Artificial intelligence and the cardiologist: what you need to know for 2020","volume":"106","author":"de Marvao","year":"2020","journal-title":"Heart"},{"issue":"4","key":"10.1016\/j.crphys.2023.100099_bib20","doi-asserted-by":"crossref","first-page":"p.e35465","DOI":"10.2196\/35465","article-title":"Contributions of artificial intelligence reported in obstetrics and gynecology journals: systematic review","volume":"24","author":"Dhombres","year":"2022","journal-title":"J. Med. Internet Research"},{"issue":"4","key":"10.1016\/j.crphys.2023.100099_bib21","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1109\/JBHI.2019.2948316","article-title":"A generic quality control framework for fetal ultrasound cardiac four-chamber planes","volume":"24","author":"Dong","year":"2019","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.crphys.2023.100099_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2019.116324","article-title":"An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI","volume":"206","author":"Ebner","year":"2020","journal-title":"Neuroimage"},{"key":"10.1016\/j.crphys.2023.100099_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104008","article-title":"Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units","volume":"78","author":"Fei","year":"2022","journal-title":"Biomed. Signal Process Control"},{"issue":"7","key":"10.1016\/j.crphys.2023.100099_bib24","doi-asserted-by":"crossref","first-page":"e368","DOI":"10.1016\/S2589-7500(20)30131-X","article-title":"Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study","volume":"2","author":"Fung","year":"2020","journal-title":"Lancet Digit. Health"},{"issue":"1","key":"10.1016\/j.crphys.2023.100099_bib25","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/uog.23739","article-title":"Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non\u2010occiput anterior positions during labor","volume":"59","author":"Ghi","year":"2022","journal-title":"Ultrasound Obstet. Gynecol."},{"issue":"1","key":"10.1016\/j.crphys.2023.100099_bib26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-71914-x","article-title":"A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants","volume":"10","author":"He","year":"2020","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.crphys.2023.100099_bib27","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1111\/j.1479-828X.2009.01122.x","article-title":"Maternal perception of foetal movement compared with movement detected by real\u2010time ultrasound: an exploratory study","volume":"50","author":"Hijazi","year":"2010","journal-title":"Aust. N. Z. J. Obstet. Gynaecol."},{"issue":"4","key":"10.1016\/j.crphys.2023.100099_bib28","doi-asserted-by":"crossref","first-page":"226","DOI":"10.4103\/ijabmr.IJABMR_370_18","article-title":"Use of machine learning algorithms for prediction of fetal risk using cardiotocographic data","volume":"9","author":"Hoodbhoy","year":"2019","journal-title":"Int. J. Appl. Basic Med. Res."},{"issue":"4","key":"10.1016\/j.crphys.2023.100099_bib29","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.bbe.2016.08.005","article-title":"Automated detection of uterine contractions in tocography signals\u2013Comparison of algorithms","volume":"36","author":"Horoba","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"issue":"3","key":"10.1016\/j.crphys.2023.100099_bib30","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.irbm.2019.04.004","article-title":"HMM-based supervised machine learning framework for the detection of fECG RR peak locations","volume":"40","author":"Huque","year":"2019","journal-title":"IRBM"},{"issue":"1","key":"10.1016\/j.crphys.2023.100099_bib31","doi-asserted-by":"crossref","first-page":"2","DOI":"10.14366\/usg.22063","article-title":"Applications of artificial intelligence in obstetrics","volume":"42","author":"Kim","year":"2022","journal-title":"Ultrasonography"},{"issue":"37","key":"10.1016\/j.crphys.2023.100099_bib32","doi-asserted-by":"crossref","first-page":"3578","DOI":"10.1093\/eurheartj\/ehac426","article-title":"CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research","volume":"43","author":"Kotecha","year":"2022","journal-title":"Eur. Heart J."},{"issue":"3","key":"10.1016\/j.crphys.2023.100099_bib33","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1515\/cclm-2019-0939","article-title":"A more accurate prediction to rule in and rule out pre-eclampsia using the sFlt-1\/PlGF ratio and NT-proBNP as biomarkers","volume":"58","author":"Lafuente-Ganuza","year":"2020","journal-title":"Clin. Chem. Lab. Med."},{"issue":"1","key":"10.1016\/j.crphys.2023.100099_bib34","first-page":"1","article-title":"Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records","volume":"11","author":"Li","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.crphys.2023.100099_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2019.101748","article-title":"Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning","volume":"102","author":"Lu","year":"2020","journal-title":"Artif. Intell. Med."},{"issue":"2","key":"10.1016\/j.crphys.2023.100099_bib36","article-title":"A comprehensive review of the role of artificial intelligence in obstetrics and gynecology","volume":"15","author":"Malani IV","year":"2023","journal-title":"Cureus"},{"issue":"2","key":"10.1016\/j.crphys.2023.100099_bib37","article-title":"Early prediction of preeclampsia via machine learning","volume":"2","author":"Mari\u0107","year":"2020","journal-title":"Am. J. Obstetr. Gynecol. MFM"},{"issue":"5","key":"10.1016\/j.crphys.2023.100099_bib38","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.tips.2020.03.003","article-title":"Electronic health records to predict gestational diabetes risk","volume":"41","author":"Mateen","year":"2020","journal-title":"Trends Pharmacol. Sci."},{"issue":"6","key":"10.1016\/j.crphys.2023.100099_bib39","article-title":"Fetal health classification from cardiotocographic data using machine learning","volume":"39","author":"Mehbodniya","year":"2022","journal-title":"Expet Syst."},{"key":"10.1016\/j.crphys.2023.100099_bib40","article-title":"Artificial intelligence applied to fetal MRI: a scoping review of current research","volume":"95","author":"Meshaka","year":"2022","journal-title":"Br. J. Radiol."},{"issue":"6","key":"10.1016\/j.crphys.2023.100099_bib41","first-page":"483","article-title":"Potential of artificial intelligence for estimating Japanese fetal weights","volume":"74","author":"Miyagi","year":"2020","journal-title":"Acta Med. Okayama"},{"issue":"14","key":"10.1016\/j.crphys.2023.100099_bib42","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.3390\/s22145103","article-title":"Accessing artificial intelligence for fetus health status using hybrid deep learning algorithm (AlexNet-SVM) on cardiotocographic data","volume":"22","author":"Muhammad Hussain","year":"2022","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.crphys.2023.100099_bib43","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1097\/EDE.0000000000000788","article-title":"Machine learning for fetal growth prediction","volume":"29","author":"Naimi","year":"2018","journal-title":"Epidemiology (Cambridge, Mass.)"},{"issue":"14","key":"10.1016\/j.crphys.2023.100099_bib44","doi-asserted-by":"crossref","first-page":"5098","DOI":"10.3390\/s22145098","article-title":"Combination of feature selection and resampling methods to predict preterm birth based on electrohysterographic signals from imbalance data","volume":"22","author":"Nieto-del-Amor","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.crphys.2023.100099_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijsu.2021.105906","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"88","author":"Page","year":"2021","journal-title":"Int. J. Surg."},{"issue":"5","key":"10.1016\/j.crphys.2023.100099_bib46","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1067\/mob.2002.122447","article-title":"A computer system for the numerical analysis of nonstress tests","volume":"186","author":"Pardey","year":"2002","journal-title":"Am. J. Obstet. Gynecol."},{"issue":"7","key":"10.1016\/j.crphys.2023.100099_bib47","doi-asserted-by":"crossref","first-page":"692","DOI":"10.3349\/ymj.2022.63.7.692","article-title":"Machine learning model for classifying the results of fetal cardiotocography conducted in high-risk pregnancies","volume":"63","author":"Park","year":"2022","journal-title":"Yonsei Med. J."},{"issue":"7","key":"10.1016\/j.crphys.2023.100099_bib48","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.3390\/s21072496","article-title":"Optimization of imminent labor prediction systems in women with threatened preterm labor based on electrohysterography","volume":"21","author":"Prats-Boluda","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.crphys.2023.100099_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103814","article-title":"A wavelet-based algorithm for automated analysis of external tocography: how does it compare to human interpretation?","volume":"122","author":"Reynolds","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.crphys.2023.100099_bib50","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.mri.2019.05.017","article-title":"Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa","volume":"64","author":"Romeo","year":"2019","journal-title":"Magn. Reson. Imag."},{"key":"10.1016\/j.crphys.2023.100099_bib51","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.crphys.2023.100099_bib52","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"720","article-title":"Real-time automatic fetal brain extraction in fetal MRI by deep learning","author":"Salehi","year":"2018"},{"key":"10.1016\/j.crphys.2023.100099_bib53","doi-asserted-by":"crossref","first-page":"47096","DOI":"10.1109\/ACCESS.2021.3067716","article-title":"Multi-modal uterine-activity measurements for prediction of embryo implantation by machine learning","volume":"9","author":"Sammali","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.crphys.2023.100099_bib54","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.ijoa.2020.08.010","article-title":"Prediction of breakthrough pain during labour neuraxial analgesia: comparison of machine learning and multivariable regression approaches","volume":"45","author":"Tan","year":"2021","journal-title":"Int. J. Obstet. Anesth."},{"issue":"4","key":"10.1016\/j.crphys.2023.100099_bib55","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1002\/uog.21878","article-title":"Development and validation of a machine\u2010learning model for prediction of shoulder dystocia","volume":"56","author":"Tsur","year":"2020","journal-title":"Ultrasound Obstet. Gynecol."},{"issue":"6","key":"10.1016\/j.crphys.2023.100099_bib56","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.compbiomed.2009.03.013","article-title":"The event synchronous canceller algorithm removes maternal ECG from abdominal signals without affecting the fetal ECG","volume":"39","author":"Ungureanu","year":"2009","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"10.1016\/j.crphys.2023.100099_bib57","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1097\/AOG.0000000000003759","article-title":"Machine learning and statistical models to predict postpartum hemorrhage","volume":"135","author":"Venkatesh","year":"2020","journal-title":"Obstet. Gynecol."},{"issue":"7","key":"10.1016\/j.crphys.2023.100099_bib58","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1109\/TPAMI.2018.2840695","article-title":"DeepIGeoS: a deep interactive geodesic framework for medical image segmentation","volume":"41","author":"Wang","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"10.1016\/j.crphys.2023.100099_bib60","doi-asserted-by":"crossref","first-page":"11526","DOI":"10.1109\/JSEN.2022.3172451","article-title":"Fetal movement detection by wearable accelerometer duo based on machine learning","volume":"22","author":"Xu","year":"2022","journal-title":"IEEE Sensor. J."},{"issue":"1","key":"10.1016\/j.crphys.2023.100099_bib61","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/TMI.2018.2858779","article-title":"Towards automated semantic segmentation in prenatal volumetric ultrasound","volume":"38","author":"Yang","year":"2018","journal-title":"IEEE Trans. Med. Imag."},{"issue":"13","key":"10.1016\/j.crphys.2023.100099_bib62","doi-asserted-by":"crossref","first-page":"4805","DOI":"10.3390\/s22134805","article-title":"Machine learning-based risk stratification for gestational diabetes management","volume":"22","author":"Yang","year":"2022","journal-title":"Sensors"},{"issue":"12","key":"10.1016\/j.crphys.2023.100099_bib63","doi-asserted-by":"crossref","first-page":"4205","DOI":"10.1007\/s00261-022-03673-4","article-title":"Prediction of placenta accreta spectrum by combining deep learning and radiomics using T2WI: a multicenter study","volume":"47","author":"Ye","year":"2022","journal-title":"Abdom. Radiol."},{"key":"10.1016\/j.crphys.2023.100099_bib64","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s11517-019-02087-7","article-title":"Single-lead noninvasive fetal ECG extraction by means of combining clustering and principal components analysis","volume":"58","author":"Zhang","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.crphys.2023.100099_bib65","article-title":"Prediction of severe preeclampsia in machine learning","volume":"15","author":"Zhang","year":"2022","journal-title":"Med. Novel Technol. Dev."},{"issue":"3","key":"10.1016\/j.crphys.2023.100099_bib66","doi-asserted-by":"crossref","DOI":"10.2196\/26634","article-title":"Machine learning prediction models for gestational diabetes mellitus: meta-analysis","volume":"24","author":"Zhang","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"10.1016\/j.crphys.2023.100099_bib68","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1007\/s13246-019-00805-x","article-title":"Fetal electrocardiography extraction with residual convolutional encoder\u2013decoder networks","volume":"42","author":"Zhong","year":"2019","journal-title":"Australas. Phys. Eng. Sci. Med."},{"issue":"2","key":"10.1016\/j.crphys.2023.100099_bib69","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1504\/IJDMB.2020.107381","article-title":"Maternal ECG removal using short time Fourier transform and convolutional auto-encoder","volume":"23","author":"Zhong","year":"2020","journal-title":"Int. J. Data Min. Bioinf."}],"container-title":["Current Research in Physiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2665944123000020?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2665944123000020?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T09:39:25Z","timestamp":1762940365000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2665944123000020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":67,"alternative-id":["S2665944123000020"],"URL":"https:\/\/doi.org\/10.1016\/j.crphys.2023.100099","relation":{},"ISSN":["2665-9441"],"issn-type":[{"value":"2665-9441","type":"print"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning and disease prediction in obstetrics","name":"articletitle","label":"Article Title"},{"value":"Current Research in Physiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.crphys.2023.100099","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"100099"}}