{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T16:59:58Z","timestamp":1793638798871,"version":"4.5.1"},"reference-count":83,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["ajog.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["American Journal of Obstetrics and Gynecology"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.ajog.2022.01.026","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T16:28:02Z","timestamp":1643732882000},"page":"77.e1-77.e30","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":84,"title":["A machine-learning\u2013based algorithm improves prediction of preeclampsia-associated adverse outcomes"],"prefix":"10.1016","volume":"227","author":[{"given":"Leon J.","family":"Schmidt","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Rieger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Neznansky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-7028","authenticated-orcid":false,"given":"Max","family":"Hackel\u00f6er","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa A.","family":"Dr\u00f6ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Henrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4262-9427","authenticated-orcid":false,"given":"David","family":"Higgins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3507-8636","authenticated-orcid":false,"given":"Stefan","family":"Verlohren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ajog.2022.01.026_bib1","doi-asserted-by":"crossref","first-page":"f6564","DOI":"10.1136\/bmj.f6564","article-title":"Pre-eclampsia rates in the United States, 1980-2010: age-period-cohort analysis","volume":"347","author":"Ananth","year":"2013","journal-title":"BMJ"},{"key":"10.1016\/j.ajog.2022.01.026_bib2","doi-asserted-by":"crossref","first-page":"b2255","DOI":"10.1136\/bmj.b2255","article-title":"Risk of pre-eclampsia in first and subsequent pregnancies: prospective cohort study","volume":"338","author":"Hern\u00e1ndez-D\u00edaz","year":"2009","journal-title":"BMJ"},{"key":"10.1016\/j.ajog.2022.01.026_bib3","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1053\/j.semperi.2009.02.010","article-title":"The global impact of pre-eclampsia and eclampsia","volume":"33","author":"Duley","year":"2009","journal-title":"Semin Perinatol"},{"key":"10.1016\/j.ajog.2022.01.026_bib4","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/S0140-6736(08)60074-4","article-title":"Epidemiology and causes of preterm birth","volume":"371","author":"Goldenberg","year":"2008","journal-title":"Lancet"},{"key":"10.1016\/j.ajog.2022.01.026_bib5","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1093\/bja\/aer192","article-title":"Saving mothers\u2019 lives: reviewing maternal deaths to make motherhood safer: 2006-8: a review","volume":"107","author":"McClure","year":"2011","journal-title":"Br J Anaesth"},{"key":"10.1016\/j.ajog.2022.01.026_bib6","first-page":"533","article-title":"Pregnancy-related mortality from preeclampsia and eclampsia","volume":"97","author":"Mackay","year":"2001","journal-title":"Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib7","first-page":"261","article-title":"Prediction of adverse outcomes by common definitions of hypertension in pregnancy","volume":"97","author":"Zhang","year":"2001","journal-title":"Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib8","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1111\/j.1479-828X.2009.01003.x","article-title":"Guidelines for the management of hypertensive disorders of pregnancy 2008","volume":"49","author":"Lowe","year":"2009","journal-title":"Aust N Z J Obstet Gynaecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib9","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S1701-2163(15)30588-0","article-title":"Diagnosis, evaluation, and management of the hypertensive disorders of pregnancy: executive summary","volume":"36","author":"Magee","year":"2014","journal-title":"J\u00a0Obstet Gynaecol Can"},{"key":"10.1016\/j.ajog.2022.01.026_bib10","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1111\/j.1751-7176.2009.00085.x","article-title":"ASH position paper: hypertension in pregnancy","volume":"11","author":"Lindheimer","year":"2009","journal-title":"J\u00a0Clin Hypertens (Greenwich)"},{"key":"10.1016\/j.ajog.2022.01.026_bib11","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.ajog.2017.04.032","article-title":"Short-term costs of preeclampsia to the United States health care system","volume":"217","author":"Stevens","year":"2017","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib12","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1186\/s12913-018-3406-1","article-title":"Economic assessment of the use of the sFlt-1\/PlGF ratio test to predict preeclampsia in Germany","volume":"18","author":"Schlembach","year":"2018","journal-title":"BMC Health Serv Res"},{"key":"10.1016\/j.ajog.2022.01.026_bib13","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1056\/NEJMoa1414838","article-title":"Predictive value of the sFlt-1:PlGF ratio in women with suspected preeclampsia","volume":"374","author":"Zeisler","year":"2016","journal-title":"N\u00a0Engl J Med"},{"key":"10.1016\/j.ajog.2022.01.026_bib14","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.preghy.2018.04.010","article-title":"Predictive value of sFlt-1, PlGF, sFlt-1\/PlGF ratio and PAPP-A for late-onset preeclampsia and IUGR between 32 and 37 weeks of pregnancy","volume":"12","author":"Birdir","year":"2018","journal-title":"Pregnancy Hypertens"},{"key":"10.1016\/j.ajog.2022.01.026_bib15","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1161\/HYPERTENSIONAHA.113.01787","article-title":"New gestational phase-specific cutoff values for the use of the soluble fms-like tyrosine kinase-1\/placental growth factor ratio as a diagnostic test for preeclampsia","volume":"63","author":"Verlohren","year":"2014","journal-title":"Hypertension"},{"key":"10.1016\/j.ajog.2022.01.026_bib16","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1111\/1471-0528.16383","article-title":"Added prognostic value of longitudinal changes of angiogenic factors in early-onset severe pre-eclampsia: a prospective cohort study","volume":"128","author":"Peguero","year":"2021","journal-title":"BJOG"},{"key":"10.1016\/j.ajog.2022.01.026_bib17","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.3109\/14767058.2011.589932","article-title":"Maternal plasma concentrations of angiogenic\/anti-angiogenic factors are of prognostic value in patients presenting to the obstetrical triage area with the suspicion of preeclampsia","volume":"24","author":"Chaiworapongsa","year":"2011","journal-title":"J\u00a0Matern Fetal Neonatal Med"},{"key":"10.1016\/j.ajog.2022.01.026_bib18","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1002\/uog.3876","article-title":"First-trimester maternal serum PP-13, PAPP-A and second-trimester uterine artery Doppler pulsatility index as markers of pre-eclampsia","volume":"29","author":"Spencer","year":"2007","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib19","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.preghy.2013.02.003","article-title":"sFlt-1\/PlGF ratio as a prognostic marker of adverse outcomes in women with early-onset preeclampsia","volume":"3","author":"De Oliveira","year":"2013","journal-title":"Pregnancy Hypertens"},{"key":"10.1016\/j.ajog.2022.01.026_bib20","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1161\/CIRCULATIONAHA.111.054361","article-title":"Angiogenic factors and the risk of adverse outcomes in women with suspected preeclampsia","volume":"125","author":"Rana","year":"2012","journal-title":"Circulation"},{"key":"10.1016\/j.ajog.2022.01.026_bib21","doi-asserted-by":"crossref","first-page":"330","DOI":"10.3109\/10641955.2016.1148162","article-title":"KRYPTOR-automated angiogenic factor assays and risk of preeclampsia-related adverse outcomes","volume":"35","author":"Salahuddin","year":"2016","journal-title":"Hypertens Pregnancy"},{"key":"10.1016\/j.ajog.2022.01.026_bib22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.preghy.2020.07.007","article-title":"sFlt-1\/PlGF ratio for the prediction of delivery within 48 hours and adverse outcomes in expectantly managed early-onset preeclampsia","volume":"22","author":"Sim\u00f3n","year":"2020","journal-title":"Pregnancy Hypertens"},{"key":"10.1016\/j.ajog.2022.01.026_bib23","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1097\/AOG.0000000000001525","article-title":"Soluble fms-like tyrosine kinase-1-to-placental growth factor ratio and time to delivery in women with suspected preeclampsia","volume":"128","author":"Zeisler","year":"2016","journal-title":"Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib24","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1161\/HYPERTENSIONAHA.111.00754","article-title":"Circulating angiogenic factors and urinary prolactin as predictors of adverse outcomes in women with preeclampsia","volume":"61","author":"Lea\u00f1os-Miranda","year":"2013","journal-title":"Hypertension"},{"key":"10.1016\/j.ajog.2022.01.026_bib25","doi-asserted-by":"crossref","first-page":"682.e1","DOI":"10.1016\/j.ajog.2017.10.001","article-title":"The prediction of fetal death with a simple maternal blood test at 20-24 weeks: a role for angiogenic index-1 (PlGF\/sVEGFR-1 ratio)","volume":"217","author":"Chaiworapongsa","year":"2017","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib26","doi-asserted-by":"crossref","first-page":"132","DOI":"10.3109\/14767058.2013.806905","article-title":"Plasma concentrations of angiogenic\/anti-angiogenic factors have prognostic value in women presenting with suspected preeclampsia to the obstetrical triage area: a prospective study","volume":"27","author":"Chaiworapongsa","year":"2014","journal-title":"J\u00a0Matern Fetal Neonatal Med"},{"key":"10.1016\/j.ajog.2022.01.026_bib27","doi-asserted-by":"crossref","first-page":"287.e1","DOI":"10.1016\/j.ajog.2013.01.016","article-title":"Maternal plasma concentrations of angiogenic\/antiangiogenic factors in the third trimester of pregnancy to identify the patient at risk for stillbirth at or near term and severe late preeclampsia","volume":"208","author":"Chaiworapongsa","year":"2013","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib28","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1161\/HYPERTENSIONAHA.119.12760","article-title":"Short-term prediction of adverse outcomes using the sFlt-1 (soluble fms-like tyrosine kinase 1)\/PlGF (placental growth factor) ratio in Asian women with suspected preeclampsia","volume":"74","author":"Bian","year":"2019","journal-title":"Hypertension"},{"key":"10.1016\/j.ajog.2022.01.026_bib29","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3109\/10641955.2013.784788","article-title":"Clinical characterization and outcomes of preeclampsia with normal angiogenic profile","volume":"32","author":"Rana","year":"2013","journal-title":"Hypertens Pregnancy"},{"key":"10.1016\/j.ajog.2022.01.026_bib30","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.clinbiochem.2019.03.005","article-title":"Circulating NT-proBNP but not soluble corin levels were associated with preeclampsia in pregnancy-associated hypertension","volume":"67","author":"Kumari","year":"2019","journal-title":"Clin Biochem"},{"key":"10.1016\/j.ajog.2022.01.026_bib31","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1080\/14767058.2016.1241992","article-title":"Early second-trimester plasma levels of NT-proBNP in women who subsequently develop early-onset preeclampsia","volume":"30","author":"Junus","year":"2017","journal-title":"J\u00a0Matern Fetal Neonatal Med"},{"key":"10.1016\/j.ajog.2022.01.026_bib32","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1515\/cclm-2019-0939","article-title":"A\u00a0more 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"},{"key":"10.1016\/j.ajog.2022.01.026_bib33","article-title":"Real-world data on the clinical use of angiogenic factors in pregnancies with placental dysfunction","author":"Dathan-Stumpf","year":"2021","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib34","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.ajog.2020.11.007","article-title":"Performance of soluble fms-like tyrosine kinase-1-to-placental growth factor ratio of \u226585 for ruling in preeclampsia within 4 weeks","volume":"224","author":"Cerdeira","year":"2021","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib35","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1161\/HYPERTENSIONAHA.120.15146","article-title":"Prediction of preeclampsia-related adverse outcomes with the sFlt-1 (soluble fms-like tyrosine kinase 1)\/PlGF (placental growth factor)-ratio in the clinical routine: a real-world study","volume":"77","author":"Dr\u00f6ge","year":"2021","journal-title":"Hypertension"},{"key":"10.1016\/j.ajog.2022.01.026_bib36","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1161\/HYPERTENSIONAHA.119.13997","article-title":"Angiogenic marker prognostic models in pregnant women with hypertension","volume":"75","author":"Perry","year":"2020","journal-title":"Hypertension"},{"key":"10.1016\/j.ajog.2022.01.026_bib37","doi-asserted-by":"crossref","first-page":"584.e1","DOI":"10.1016\/j.ajog.2019.01.235","article-title":"Prediction of imminent preeclampsia at 35-37 weeks gestation","volume":"220","author":"Ciobanu","year":"2019","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib38","doi-asserted-by":"crossref","first-page":"199.e1","DOI":"10.1016\/j.ajog.2018.11.1087","article-title":"Predictive performance of the competing risk model in screening for preeclampsia","volume":"220","author":"Wright","year":"2019","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib39","doi-asserted-by":"crossref","first-page":"585.e1","DOI":"10.1016\/j.ajog.2017.07.038","article-title":"Aspirin for Evidence-Based Preeclampsia Prevention trial: effect of aspirin in prevention of preterm preeclampsia in subgroups of women according to their characteristics and medical and obstetrical history","volume":"217","author":"Poon","year":"2017","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib40","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.preghy.2021.05.021","article-title":"Relevance of maternal sodium level for preeclampsia-associated adverse pregnancy outcomes","volume":"25","author":"Antonia Dr\u00f6ge","year":"2021","journal-title":"Pregnancy Hypertens"},{"key":"10.1016\/j.ajog.2022.01.026_bib41","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1002\/uog.23142","article-title":"Prediction of pre-eclampsia-related complications in women with suspected or confirmed pre-eclampsia: development and internal validation of clinical prediction model","volume":"58","author":"Saleh","year":"2021","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib42","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1002\/pd.4554","article-title":"Modeling risk for severe adverse outcomes using angiogenic factor measurements in women with suspected preterm preeclampsia","volume":"35","author":"Palomaki","year":"2015","journal-title":"Prenat Diagn"},{"key":"10.1016\/j.ajog.2022.01.026_bib43","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1002\/uog.20157","article-title":"Fetal Medicine Foundation reference ranges for umbilical artery and middle cerebral artery pulsatility index and cerebroplacental ratio","volume":"53","author":"Ciobanu","year":"2019","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib44","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-540-24775-3_3","article-title":"Evaluating the replicability of significance tests for comparing learning algorithms","author":"Bouckaert","year":"2004","journal-title":"Adv Knowl Discov Data Min"},{"key":"10.1016\/j.ajog.2022.01.026_bib45","article-title":"Inference for the generalization error","author":"Nadeau","year":"1999","journal-title":"Montr\u00e9al: CIRANO"},{"key":"10.1016\/j.ajog.2022.01.026_bib46","doi-asserted-by":"crossref","first-page":"2100042","DOI":"10.1002\/aisy.202100042","article-title":"OnRAMP for regulating artificial intelligence in medical products","volume":"3","author":"Higgins","year":"2021","journal-title":"Adv Intell Syst"},{"key":"10.1016\/j.ajog.2022.01.026_bib47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"10.1016\/j.ajog.2022.01.026_bib48","series-title":"Ensemble machine learning","first-page":"157","article-title":"Random forests","author":"Cutler","year":"2012"},{"key":"10.1016\/j.ajog.2022.01.026_bib49","first-page":"278","article-title":"Random decision forests","volume":"1","author":"Ho","year":"1995","journal-title":"ICDAR"},{"key":"10.1016\/j.ajog.2022.01.026_bib50","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1162\/neco.1997.9.7.1545","article-title":"Shape quantization and recognition with randomized trees","volume":"9","author":"Amit","year":"1997","journal-title":"Neural Comput"},{"key":"10.1016\/j.ajog.2022.01.026_bib51","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1023\/A:1018054314350","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach Learn"},{"key":"10.1016\/j.ajog.2022.01.026_bib52","first-page":"785","article-title":"XGBoost: a scalable tree boosting system","volume":"11","author":"Chen","year":"2016","journal-title":"Proc ACM SIGKDD Int"},{"key":"10.1016\/j.ajog.2022.01.026_bib53","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","article-title":"Explainable machine-learning predictions for the prevention of hypoxaemia during surgery","volume":"2","author":"Lundberg","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.ajog.2022.01.026_bib54","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.ajog.2022.01.026_bib55","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-3-030-57321-8_2","article-title":"The explanation game: explaining machine learning models using Shapley values","author":"Merrick","year":"2020","journal-title":"Lect Notes Comput Sci"},{"key":"10.1016\/j.ajog.2022.01.026_bib56","series-title":"Game theory","first-page":"210","article-title":"Shapley value","volume":"1","author":"Hart","year":"1989"},{"key":"10.1016\/j.ajog.2022.01.026_bib57","series-title":"The Shapley value: essays in honor of Lloyd S. Shapley","year":"1988"},{"key":"10.1016\/j.ajog.2022.01.026_bib58","series-title":"Values of non-atomic games","author":"Aumann","year":"2016"},{"key":"10.1016\/j.ajog.2022.01.026_bib59","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s003550000070","article-title":"The value of a player in n-person games","volume":"18","author":"Hausken","year":"2001","journal-title":"Soc Choice Welf"},{"key":"10.1016\/j.ajog.2022.01.026_bib60","first-page":"28","article-title":"The generalisation of student's problems when several different population variances are involved","volume":"34","author":"Welch","year":"1947","journal-title":"Biometrika"},{"key":"10.1016\/j.ajog.2022.01.026_bib61","doi-asserted-by":"crossref","first-page":"161.e1","DOI":"10.1016\/j.ajog.2009.09.016","article-title":"An automated method for the determination of the sFlt-1\/PIGF ratio in the assessment of preeclampsia","volume":"202","author":"Verlohren","year":"2010","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib62","doi-asserted-by":"crossref","first-page":"58.e1","DOI":"10.1016\/j.ajog.2011.07.037","article-title":"The sFlt-1\/PlGF ratio in different types of hypertensive pregnancy disorders and its prognostic potential in preeclamptic patients","volume":"206","author":"Verlohren","year":"2012","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib63","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1002\/uog.15663","article-title":"Biophysical and biochemical markers at 35-37 weeks\u2019 gestation in the prediction of adverse perinatal outcome","volume":"47","author":"Vali\u00f1o","year":"2016","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib64","doi-asserted-by":"crossref","first-page":"89.e1","DOI":"10.1016\/j.ajog.2016.01.168","article-title":"Sequential plasma angiogenic factors levels in women with suspected preeclampsia","volume":"215","author":"Baltajian","year":"2016","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib65","doi-asserted-by":"crossref","first-page":"305.e1","DOI":"10.1016\/j.ajog.2021.03.041","article-title":"Angiogenic markers and their longitudinal change for predicting adverse outcomes in pregnant women with chronic hypertension","volume":"225","author":"Binder","year":"2021","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib66","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0221202","article-title":"Prediction model development of late-onset preeclampsia using machine learning-based methods","volume":"14","author":"Jhee","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.ajog.2022.01.026_bib67","doi-asserted-by":"crossref","first-page":"100100","DOI":"10.1016\/j.ajogmf.2020.100100","article-title":"Early\u00a0prediction of preeclampsia via machine learning","volume":"2","author":"Mari\u0107","year":"2020","journal-title":"Am J Obstet Gynecol MFM"},{"key":"10.1016\/j.ajog.2022.01.026_bib68","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0225716","article-title":"Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: a population based cohort study","volume":"14","author":"Sandstr\u00f6m","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.ajog.2022.01.026_bib69","doi-asserted-by":"crossref","first-page":"518.e1","DOI":"10.1016\/j.ajog.2020.11.004","article-title":"Impact of new definitions of preeclampsia at term on identification of adverse maternal and perinatal outcomes","volume":"224","author":"Lai","year":"2021","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib70","article-title":"The diagnostic value of angiogenic and antiangiogenic factors in differential diagnosis of preeclampsia","author":"Verlohren","year":"2020","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.ajog.2022.01.026_bib72","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202000052","article-title":"From bit to bedside: a practical framework for artificial intelligence product development in healthcare","volume":"2","author":"Higgins","year":"2020","journal-title":"Adv Intell Syst"},{"key":"10.1016\/j.ajog.2022.01.026_bib73","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1093\/idpl\/ipx022","article-title":"Meaningful information and the right to explanation","volume":"7","author":"Selbst","year":"2017","journal-title":"Int Data Privacy Law"},{"key":"10.1016\/j.ajog.2022.01.026_bib74","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1038\/s41746-019-0158-1","article-title":"Why digital medicine depends on interoperability","volume":"2","author":"Lehne","year":"2019","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.ajog.2022.01.026_bib75","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1023\/A:1022643204877","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach Learn"},{"key":"10.1016\/j.ajog.2022.01.026_bib76","first-page":"785","article-title":"XGBoost: A scalable tree boosting system","volume":"11","author":"Chen","year":"2016","journal-title":"Proc ACM SIGKDD Int"},{"key":"10.1016\/j.ajog.2022.01.026_bib77","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-540-24775-3_3","article-title":"Evaluating the replicability of significance tests for comparing learning algorithms","author":"Bouckaert","year":"2004","journal-title":"Adv Knowl Discov Data Min"},{"key":"10.1016\/j.ajog.2022.01.026_bib78","article-title":"Inference for the Generalization Error","author":"Nadeau","year":"1999","journal-title":"Montr\u00e9al: CIRANO"},{"key":"10.1016\/j.ajog.2022.01.026_bib79","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MCSE.2007.58","article-title":"Python for scientific computing","volume":"9","author":"Oliphant","year":"2007","journal-title":"Comput Sci Eng"},{"key":"10.1016\/j.ajog.2022.01.026_bib80","unstructured":"Oliphant TE. Guide to NumPy. 2nd ed. CreateSpace Independent Publishing Platform; 2015."},{"key":"10.1016\/j.ajog.2022.01.026_bib81","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCSE.2011.37","article-title":"The\u00a0NumPy array: a structure for efficient numerical computation","volume":"13","author":"van der Walt","year":"2011","journal-title":"Comput Sci Eng"},{"key":"10.1016\/j.ajog.2022.01.026_bib82","first-page":"51","article-title":"Data structures for statistical computing in Python","volume":"445","author":"McKinney","year":"2010","journal-title":"Proc. Python Sci. Conf"},{"key":"10.1016\/j.ajog.2022.01.026_bib83","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J\u00a0Mach Learn Res"},{"key":"10.1016\/j.ajog.2022.01.026_bib85","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1038\/s41592-021-01205-4","article-title":"DOME: recommendations for supervised machine learning validation in biology","volume":"18","author":"Walsh","year":"2021","journal-title":"Nat Methods"}],"container-title":["American Journal of Obstetrics and Gynecology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0002937822000503?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0002937822000503?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:37:13Z","timestamp":1757590633000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0002937822000503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":83,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["S0002937822000503"],"URL":"https:\/\/doi.org\/10.1016\/j.ajog.2022.01.026","relation":{},"ISSN":["0002-9378"],"issn-type":[{"value":"0002-9378","type":"print"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A machine-learning\u2013based algorithm improves prediction of preeclampsia-associated adverse outcomes","name":"articletitle","label":"Article Title"},{"value":"American Journal of Obstetrics and Gynecology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ajog.2022.01.026","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}