{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:06:04Z","timestamp":1790255164226,"version":"4.1.0"},"reference-count":68,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Birth Defects Research"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>With approximately 50% of pregnancies being unplanned, there is an unintended exposure to potential feto\u2010toxic drugs that may cause major congenital malformations (MCM). This study aims to compare the predictive performance between traditional pharmacoepidemiologic (PE) and machine learning (ML) models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a cohort study within the Quebec Pregnancy Cohort, including all pregnancies covered by Quebec's prescription drug insurance program and their children from 01\/1998 to 12\/2015. Medication exposures, comorbidities, and women's characteristics 12\u2009months before pregnancy and during the first trimester were considered. Robust Poisson models were used to obtain adjusted risk ratios (aRR) and 95% confidence intervals (CI) of predictors. Logistic regression, robust Poisson, K\u2010Nearest Neighbors, Random Forest, XGBoost, Na\u00efve bayes, Multilayer Perceptron, and Support Vector Machine were developed to predict pregnancies at risk of MCM. Sensitivity, specificity, PPV, NPV, accuracy, ROC\u2010AUCs, PR\u2010AUCs, and F1\u2010score were used to evaluate the performance of predictive models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We analyzed 213,744 pregnancies, finding a 9.7% prevalence of MCM. Logistic regression had the highest discriminative power across models at predicting MCM, with a ROC\u2010AUC of 53.3% and the highest sensitivity (41.5%) and F1\u2010score (46.6%). KNN had the highest specificity (97.9%) but the lowest sensitivity (2.3%). Robust Poisson performed similarly to logistic regression, with the highest accuracy (52.3%). Robust Poisson performed slightly better than logistic regression at classifying organ\u2010specific malformations. All models showed poor overall predictive performance. Results were robust across sensitivity analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>There is insufficient evidence for the superiority of ML over traditional pharmacoepidemiologic modeling in predicting MCM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bdr2.70117","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T04:55:56Z","timestamp":1788843356000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Performance of Traditional Pharmacoepidemiologic and Machine Learning Models to Predict Pregnancies at Risk of Major Congenital Malformations"],"prefix":"10.1002","volume":"118","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0999-3272","authenticated-orcid":false,"given":"Gabra","family":"Nohmie","sequence":"first","affiliation":[{"name":"Medications and Pregnancy Unit CHU Sainte\u2010Justine Azrieli Research Center  Montr\u00e9al Quebec Canada"},{"name":"Faculty of Pharmacy University of Montr\u00e9al  Montr\u00e9al Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-0314","authenticated-orcid":false,"given":"Marc","family":"Lanovaz","sequence":"additional","affiliation":[{"name":"Faculty of Arts and Sciences University of Montr\u00e9al  Montr\u00e9al Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6784-2080","authenticated-orcid":false,"given":"Odile","family":"Sheehy","sequence":"additional","affiliation":[{"name":"Medications and Pregnancy Unit CHU Sainte\u2010Justine Azrieli 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Quebec Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5018-0108","authenticated-orcid":false,"given":"Christine","family":"Damase\u2010Michel","sequence":"additional","affiliation":[{"name":"CHU Toulouse III, INSERM CERPOP  Toulouse France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7535-5517","authenticated-orcid":false,"given":"Anick","family":"B\u00e9rard","sequence":"additional","affiliation":[{"name":"Medications and Pregnancy Unit CHU Sainte\u2010Justine Azrieli Research Center  Montr\u00e9al Quebec Canada"},{"name":"Faculty of Pharmacy University of Montr\u00e9al  Montr\u00e9al Quebec 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