{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T09:35:19Z","timestamp":1773135319268,"version":"3.50.1"},"reference-count":16,"publisher":"Federa\u00e7\u00e3o das Associa\u00e7\u00f5es de Ginecologia e Obstetr\u00edcia","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,4]]},"DOI":"10.61622\/rbgo\/2026rbgo12","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T19:38:19Z","timestamp":1773085099000},"source":"Crossref","is-referenced-by-count":0,"title":["Diagnosing ectopic pregnancy using the bayes theorem and neural network: a validation of a retrospective cohort study"],"prefix":"10.61622","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8918-6897","authenticated-orcid":false,"given":"Larissa","family":"Maroni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4195-624X","authenticated-orcid":false,"suffix":"Neto","given":"Pedro Clarindo","family":"Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6180-4104","authenticated-orcid":false,"given":"Rafael","family":"Kunst","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2779-5443","authenticated-orcid":false,"given":"Ricardo Francalacci","family":"Savaris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"47794","reference":[{"issue":"8","key":"key20260309193811_B1","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1503\/cmaj.050222","article-title":"Diagnosis and treatment of ectopic pregnancy","volume":"173","author":"Murray","year":"2005","unstructured":"1 Murray H, Baakdah H, Bardell T, Tulandi T. Diagnosis and treatment of ectopic pregnancy. CMAJ. 2005;173(8):905-12. doi: 10.1503\/cmaj.050222","journal-title":"CMAJ"},{"issue":"10","key":"key20260309193811_B2","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1097\/OGX.0000000000000492","article-title":"New evidence to guide ectopic pregnancy diagnosis and management","volume":"72","author":"Brady","year":"2017","unstructured":"2 Brady PC. New evidence to guide ectopic pregnancy diagnosis and management. Obstet Gynecol Surv. 2017;72(10):618-25. doi: 10.1097\/OGX.0000000000000492","journal-title":"Obstet Gynecol Surv"},{"issue":"7","key":"key20260309193811_B3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.ogrm.2020.03.011","article-title":"Ectopic pregnancy","volume":"30","author":"Layden","year":"2020","unstructured":"3 Layden E, Madhra M. Ectopic pregnancy. Obstet Gynaecol Reprod Med. 2020;30(7):205-12. doi: 10.1016\/j.ogrm.2020.03.011","journal-title":"Obstet Gynaecol Reprod Med"},{"issue":"1","key":"key20260309193811_B4","doi-asserted-by":"crossref","first-page":"78","DOI":"10.14366\/usg.17044","article-title":"Diagnosing ectopic pregnancy in the emergency setting","volume":"37","author":"Lee","year":"2018","unstructured":"4 Lee R, Dupuis C, Chen B, Smith A, Kim YH. Diagnosing ectopic pregnancy in the emergency setting. Ultrasonography. 2018;37(1):78-87. doi: 10.14366\/usg.17044","journal-title":"Ultrasonography"},{"key":"key20260309193811_B5","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-80928-7","volume-title":"Machine learning for critical Internet of medical things: applications and use cases","author":"Al-Turjman","year":"2022","unstructured":"5 Al-Turjman F, Nayyar A. Machine learning for critical Internet of medical things: applications and use cases. Cham: Springer Nature; 2022 [cited 2025 Apr 8]. Available from: https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80928-7.pdf"},{"issue":"1","key":"key20260309193811_B6","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s13073-021-00968-x","article-title":"Deep learning in cancer diagnosis, prognosis and treatment selection","volume":"13","author":"Tran","year":"2021","unstructured":"6 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13(1):152. doi: 10.1186\/s13073-021-00968-x","journal-title":"Genome Med"},{"issue":"4","key":"key20260309193811_B7","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1177\/0300985819829524","article-title":"Review of medical decision support and machine-learning methods","volume":"56","author":"Awaysheh","year":"2019","unstructured":"7 Awaysheh A, Wilcke J, Elvinger F, Rees L, Fan W, Zimmerman KL. Review of medical decision support and machine-learning methods. Vet Pathol. 2019;56(4):512-25. doi: 10.1177\/0300985819829524","journal-title":"Vet Pathol"},{"issue":"1","key":"key20260309193811_B8","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.fertnstert.2022.09.016","article-title":"Diagnosing ectopic pregnancy using Bayes theorem: a retrospective cohort study","volume":"119","author":"Link","year":"2023","unstructured":"8 Link CA, Maissiat J, Mol BW, Barnhart KT, Savaris RF. Diagnosing ectopic pregnancy using Bayes theorem: a retrospective cohort study. Fertil Steril. 2023;119(1):78-86. doi: 10.1016\/j.fertnstert.2022.09.016","journal-title":"Fertil Steril"},{"key":"key20260309193811_B9","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-3-030-85292-4_3","article-title":"Foundations of machine learning-based clinical prediction modeling: Part II-generalization and overfitting","volume":"134","author":"Kernbach","year":"2022","unstructured":"9 Kernbach JM, Staartjes VE. Foundations of machine learning-based clinical prediction modeling: Part II-generalization and overfitting. Acta Neurochir Suppl. 2022;134:15-21. doi: 10.1007\/978-3-030-85292-4_3","journal-title":"Acta Neurochir Suppl"},{"key":"key20260309193811_B10","article-title":"Diagnosing ectopic pregnancy using the Bayes theorem: a retrospective cohort study","author":"Savaris","year":"2021","unstructured":"10 Savaris R. Diagnosing ectopic pregnancy using the Bayes theorem: a retrospective cohort study. Harvard Dataverse [Submit for Review]. 2021 [cited 2025 Apr 8]. Available from: https:\/\/dataverse.harvard.edu\/dataset.xhtml?persistentId=doi:10.7910\/DVN\/HSCPKZ","journal-title":"Harvard Dataverse [Submit for Review]"},{"key":"key20260309193811_B11","first-page":"2349","article-title":"Orange: data mining toolbox in Python","volume":"14","author":"Dem\u0161ar","year":"2013","unstructured":"11 Dem\u0161ar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, et al. Orange: data mining toolbox in Python. J Mach Learn Res. 2013;14:2349\u221253.","journal-title":"J Mach Learn Res"},{"key":"key20260309193811_B12","first-page":"976829","article-title":"Predictive analytical model for ectopic pregnancy diagnosis: Statistics vs","volume":"9","author":"Rueangket","year":"2022","unstructured":"12 Rueangket P, Rittiluechai K, Prayote A. Predictive analytical model for ectopic pregnancy diagnosis: Statistics vs. machine learning. Front Med. 2022;9:976829. doi: 10.3389\/fmed.2022.976829","journal-title":"machine learning. Front Med"},{"issue":"5","key":"key20260309193811_B13","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1080\/02533839.2025.2479118","article-title":"Enhancing ectopic pregnancy detection: a machine learning approach using ultrasound imaging","volume":"48","author":"Suresh","year":"2025","unstructured":"13 Suresh LR, Lakshmanan SK. Enhancing ectopic pregnancy detection: a machine learning approach using ultrasound imaging. J Chin Inst Eng. 2025;48(5):599-611. doi: 10.1080\/02533839.2025.2479118","journal-title":"J Chin Inst Eng"},{"key":"key20260309193811_B14","article-title":"Development and validation of an AI-based decision support tool (PROMETHEUS) for predicting methotrexate success in ectopic pregnancy","author":"Odaba\u015f","year":"2025","unstructured":"14 Odaba\u015f O, Kanmaz AK, Toz E, \u0130nan AH, Akpak YK, Kanmaz AG. Development and validation of an AI-based decision support tool (PROMETHEUS) for predicting methotrexate success in ectopic pregnancy. Authorea [Preprint]. 2025 [cited 2025 Apr 8]. Available from: https:\/\/www.authorea.com\/users\/908756\/articles\/1282623-development-and-validation-of-an-ai-based-decision-support-tool-prometheus-for-predicting-methotrexate-success-in-ectopic-pregnancy","journal-title":"Authorea [Preprint]"},{"key":"key20260309193811_B15","first-page":"4765","volume-title":"Advances in neural information processing systems","author":"Lundberg","year":"2017","unstructured":"15 Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, et al., editors. Advances in neural information processing systems. New York: Curran Associates; 2017. p. 4765-74."},{"issue":"13","key":"key20260309193811_B16","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big data and machine learning in health care","volume":"319","author":"Beam","year":"2018","unstructured":"16 Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-8. doi: 10.1001\/jama.2017.18391","journal-title":"JAMA"}],"container-title":["Revista Brasileira de Ginecologia e Obstetr\u00edcia"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T19:38:22Z","timestamp":1773085102000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalrbgo.org\/article\/diagnosing-ectopic-pregnancy-using-the-bayes-theorem-and-neural-network-a-validation-of-a-retrospective-cohort-study\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":16,"alternative-id":["10.61622\/rbgo\/2026rbgo12"],"URL":"https:\/\/doi.org\/10.61622\/rbgo\/2026rbgo12","relation":{},"ISSN":["0100-7203","1806-9339"],"issn-type":[{"value":"0100-7203","type":"print"},{"value":"1806-9339","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"article-number":"e-rbgo12"}}