{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T03:48:05Z","timestamp":1790048885599,"version":"4.0.1"},"reference-count":33,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Twin Res Hum Genet"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This study aimed to create a risk prediction model with artificial intelligence (AI) to identify patients at higher risk of postpartum hemorrhage using perinatal characteristics that may be associated with later postpartum hemorrhage (PPH) in twin pregnancies that underwent cesarean section. The study was planned as a retrospective cohort study at University Hospital. All twin cesarean deliveries were categorized into two groups: those with and without PPH. Using the perinatal characteristics of the cases, four different machine learning classifiers were created: Logistic regression (LR), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP). LR, RF, and SVM models were created a second time by including class weights to manage the underlying imbalances in the data. A total of 615 twin pregnancies were included in the study. There were 150 twin pregnancies with PPH and 465 without PPH. Dichorionity, PAS, and placenta previa were significantly higher in the PPH-positive group (<jats:italic>p<\/jats:italic> = .045, <jats:italic>p<\/jats:italic> = .004, <jats:italic>p<\/jats:italic> = .001 respectively). In our model, LR with class weight was the best model with the highest negative predictive value. The AUC in our LR with class weight model was %75.12 with an accuracy of 70.73%, a PPV of 47.92%, and an NPV of 85.33% in our data. Although the application of machine learning to create predictive models using clinical risk factors and our model\u2019s 70% accuracy rate are encouraging, it is not sufficient. Machine learning modeling needs further study and validation before being incorporated into clinical use.<\/jats:p>","DOI":"10.1017\/thg.2024.48","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T07:26:57Z","timestamp":1736926017000},"page":"53-59","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":6,"title":["Artificial Intelligence in Predicting Postpartum Hemorrhage in Twin Pregnancies Undergoing Cesarean Section"],"prefix":"10.1017","volume":"28","author":[{"given":"Sukran","family":"Dogru","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7626-5799","authenticated-orcid":false,"given":"Huriye","family":"Ezveci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatih","family":"Akkus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pelin","family":"Bah\u00e7eci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fikriye","family":"Karanfil Yaman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Acar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,1,15]]},"reference":[{"key":"S1832427424000483_ref8","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1097\/AOG.0b013e3182941c78","article-title":"Clinical validation of risk stratification criteria for peripartum hemorrhage","volume":"122","author":"Dilla","year":"2013","journal-title":"Obstetrics and Gynecology"},{"key":"S1832427424000483_ref11","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1097\/AOG.0000000000004228","article-title":"Risk factors for atonic postpartum hemorrhage: A systematic review and meta-analysis","volume":"137","author":"Ende","year":"2021","journal-title":"Obstetrics and Gynecology"},{"key":"S1832427424000483_ref4","doi-asserted-by":"crossref","first-page":"876411","DOI":"10.3389\/fmed.2022.876411","article-title":"Twin growth discordance and risk of postpartum hemorrhage: A retrospective cohort study","volume":"9","author":"Cao","year":"2022","journal-title":"Frontiers in Medicine"},{"key":"S1832427424000483_ref10","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1007\/s00404-015-3771-5","article-title":"Predictors of severity in primary postpartum hemorrhage","volume":"292","author":"Ekin","year":"2015","journal-title":"Arch Gynecol Obstet"},{"key":"S1832427424000483_ref2","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1111\/1471-0528.15607","article-title":"Predicting common maternal postpartum complications: Leveraging health administrative data and machine learning","volume":"126","author":"Betts","year":"2019","journal-title":"BJOG"},{"key":"S1832427424000483_ref26","doi-asserted-by":"crossref","first-page":"e323","DOI":"10.1016\/S2214-109X(14)70227-X","article-title":"Global causes of maternal death: a WHO systematic analysis","volume":"2","author":"Say","year":"2014","journal-title":"Lancet Global Health"},{"key":"S1832427424000483_ref28","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4899-7641-3","volume-title":"Machine learning models and algorithms for big data classification","author":"Suthaharan","year":"2016"},{"key":"S1832427424000483_ref30","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":"Obstetrics and Gynecology"},{"key":"S1832427424000483_ref12","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.tjog.2018.03.002","article-title":"Effect of twin pregnancy chorionic properties on maternal and fetal outcomes","volume":"57","author":"Feng","year":"2018","journal-title":"Taiwanese Journal of Obstetrics and Gynecology"},{"key":"S1832427424000483_ref18","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s00404-021-06377-0","article-title":"Machine learning-based prediction of postpartum hemorrhage after vaginal delivery: Combining bleeding high risk factors and uterine contraction curve","volume":"306","author":"Liu","year":"2022","journal-title":"Archives of Gynecology and Obstetrics"},{"key":"S1832427424000483_ref21","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1097\/AOG.0000000000004204","article-title":"Placenta accreta spectrum among women with twin gestations","volume":"137","author":"Miller","year":"2021","journal-title":"Obstetrics and Gynecology"},{"key":"S1832427424000483_ref3","doi-asserted-by":"crossref","first-page":"3740","DOI":"10.1080\/14767058.2019.1583736","article-title":"Twin pregnancy and risk of postpartum hemorrhage","volume":"33","author":"Blitz","year":"2020","journal-title":"Journal of Maternal-Fetal and Neonatal Medicine"},{"key":"S1832427424000483_ref27","doi-asserted-by":"crossref","first-page":"367","DOI":"10.17085\/apm.23019","article-title":"Risk factors for severe postpartum hemorrhage requiring blood transfusion after cesarean delivery for twin pregnancy: A nationwide cohort study","volume":"18","author":"Seo","year":"2023","journal-title":"Anesthesia and Pain Medicine"},{"key":"S1832427424000483_ref13","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1111\/aogs.14116","article-title":"Obstetric outcomes for twins from different conception methods \u00be A multicenter cross-sectional study from China","volume":"100","author":"Jiang","year":"2021","journal-title":"Acta Obstetricia et Gynecologica Scandinavica"},{"key":"S1832427424000483_ref6","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1158\/1940-6207.CAPR-19-0556","article-title":"Intercepting endometrial cancer: Opportunities to expand access using new technology","volume":"13","author":"DeStephano","year":"2020","journal-title":"Cancer Prevention Research"},{"key":"S1832427424000483_ref16","doi-asserted-by":"crossref","DOI":"10.1016\/j.ajog.2013.07.007","article-title":"Incidence, risk factors, and temporal trends in severe postpartum hemorrhage","volume":"209","author":"Kramer","year":"2013","journal-title":"American Journal of Obstetrics and Gynecology"},{"key":"S1832427424000483_ref7","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics13030446","article-title":"Multiple pregnancy and the risk of postpartum hemorrhage: Retrospective analysis in a tertiary level center of care","volume":"13","author":"di Marco","year":"2023","journal-title":"Diagnostics"},{"key":"S1832427424000483_ref5","doi-asserted-by":"crossref","first-page":"e168","DOI":"10.1097\/AOG.0000000000002351","article-title":"Practice Bulletin No. 183: Postpartum hemorrhage","volume":"130","year":"2017","journal-title":"Obstetrics and Gynecology"},{"key":"S1832427424000483_ref17","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1186\/s12884-022-04775-z","article-title":"A novel oppositional binary crow search algorithm with optimal machine learning based postpartum hemorrhage prediction model","volume":"22","author":"Krishnamoorthy","year":"2022","journal-title":"BMC Pregnancy Childbirth"},{"key":"S1832427424000483_ref22","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1093\/humrep\/deab029","article-title":"Twin Peaks: more twinning in humans than ever before","volume":"36","author":"Monden","year":"2021","journal-title":"Human Reproduction"},{"key":"S1832427424000483_ref1","first-page":"1039","article-title":"ACOG Practice Bulletin: Clinical Management Guidelines for Obstetrician-Gynecologists Number 76, October 2006: postpartum hemorrhage","volume":"108","year":"2006","journal-title":"Obstetrics and Gynecology"},{"key":"S1832427424000483_ref19","doi-asserted-by":"crossref","first-page":"e36307","DOI":"10.1097\/MD.0000000000036307","article-title":"Predicting risk of postpartum hemorrhage associated with vaginal delivery of twins: A retrospective study","volume":"102","author":"Liu","year":"2023","journal-title":"Medicine (Baltimore)"},{"key":"S1832427424000483_ref23","doi-asserted-by":"crossref","unstructured":"Qi, F. , Tian, D. , Wang, Y. , & Jiao, L. (2018, October 19\u201321). Prediction of postpartum hemorrhage by adaptive K-nearest neighbor based on influence factors [Paper presentation]. 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China.","DOI":"10.1109\/ITME.2018.00202"},{"key":"S1832427424000483_ref24","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/978-1-4419-9326-7_11","volume-title":"Ensemble machine learning: Methods and applications","author":"Qi","year":"2012"},{"key":"S1832427424000483_ref29","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","article-title":"Extreme learning machine for multilayer perceptron","volume":"27,","author":"Tang","year":"2015","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"S1832427424000483_ref31","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1530\/REP-18-0523","article-title":"Artificial intelligence in reproductive medicine","volume":"158","author":"Wang","year":"2019","journal-title":"Reproduction"},{"key":"S1832427424000483_ref15","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1111\/aogs.12352","article-title":"Prediction of postpartum hemorrhage in women with gestational hypertension or mild preeclampsia at term","volume":"93","author":"Koopmans","year":"2014","journal-title":"Acta Obstetricia et Gynecologica Scandinavica"},{"key":"S1832427424000483_ref14","first-page":"295","article-title":"Risk factors for postpartum haemorrhage in twin pregnancies and haemorrhage severity","volume":"29","author":"Kong","year":"2023","journal-title":"Hong Kong Medical Journal"},{"key":"S1832427424000483_ref33","doi-asserted-by":"crossref","first-page":"104966","DOI":"10.1016\/j.cageo.2021.104966","article-title":"Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China","volume":"158","author":"Zhang","year":"2022","journal-title":"Computers and Geosciences"},{"key":"S1832427424000483_ref25","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.ijnurstu.2017.11.009","article-title":"Development and validation of a predictive model for excessive postpartum blood loss: A retrospective, cohort study","volume":"79","author":"Rubio-\u00c1lvarez","year":"2018","journal-title":"International Journal of Nursing Studies"},{"key":"S1832427424000483_ref9","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1097\/AOG.0000000000001844","article-title":"Association of intended route of delivery and maternal morbidity in twin pregnancy","volume":"129","author":"Easter","year":"2017","journal-title":"Obstetrics and Gynecology, l"},{"key":"S1832427424000483_ref32","doi-asserted-by":"crossref","first-page":"e34108","DOI":"10.2196\/34108","article-title":"Prediction of maternal hemorrhage using machine learning: Retrospective cohort study","volume":"24","author":"Westcott","year":"2022","journal-title":"Journal of Medical Internet Research"},{"key":"S1832427424000483_ref20","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1182\/hematology.2020000139","article-title":"Prevention and treatment of postpartum hemorrhage: focus on hematological aspects of management","volume":"2020","author":"McLintock","year":"2020","journal-title":"Hematology ASH Education Program"}],"container-title":["Twin Research and Human Genetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S1832427424000483","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T07:39:52Z","timestamp":1745825992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S1832427424000483\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,15]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["S1832427424000483"],"URL":"https:\/\/doi.org\/10.1017\/thg.2024.48","relation":{},"ISSN":["1832-4274","1839-2628"],"issn-type":[{"value":"1832-4274","type":"print"},{"value":"1839-2628","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,15]]},"assertion":[{"value":"\u00a9 The Author(s), 2025. Published by Cambridge University Press on behalf of International Society for Twin Studies","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}}]}}