{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:42:28Z","timestamp":1778496148935,"version":"3.51.4"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Teratogenic drugs can cause severe fetal malformation and therefore have critical impact on the health of the fetus, yet the teratogenic risks are unknown for most approved drugs. This article proposes an explainable machine learning model for classifying pregnancy drug safety based on multimodal data and suggests an orthogonal ensemble for modeling multimodal data. To train the proposed model, we created a set of labeled drugs by processing over 100\u00a0000 textual responses collected by a large teratology information service. Structured textual information is incorporated into the model by applying clustering analysis to textual features.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We report an area under the receiver operating characteristic curve (AUC) of 0.891 using cross-validation and an AUC of 0.904 for cross-expert validation. Our findings suggest the safety of two drugs during pregnancy, Varenicline and Mebeverine, and suggest that Meloxicam, an NSAID, is of higher risk; according to existing data, the safety of these three drugs during pregnancy is unknown. We also present a web-based application that enables physicians to examine a specific drug and its risk factors.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code and data is available from https:\/\/github.com\/goolig\/drug_safety_pregnancy_prediction.git.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab769","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T12:23:59Z","timestamp":1636115039000},"page":"1102-1109","source":"Crossref","is-referenced-by-count":15,"title":["Explainable multimodal machine learning model for classifying pregnancy drug safety"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0647-8753","authenticated-orcid":false,"given":"Guy","family":"Shtar","sequence":"first","affiliation":[{"name":"Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev , Beer-Shevam, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lior","family":"Rokach","sequence":"additional","affiliation":[{"name":"Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev , Beer-Shevam, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bracha","family":"Shapira","sequence":"additional","affiliation":[{"name":"Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev , Beer-Shevam, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elkana","family":"Kohn","sequence":"additional","affiliation":[{"name":"Clinical Pharmacology and Toxicology Unit, Drug Consultation Center, Shamir Medical Center (Assaf Harofeh), Zerifin, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matitiahu","family":"Berkovitch","sequence":"additional","affiliation":[{"name":"Clinical Pharmacology and Toxicology Unit, Drug Consultation Center, Shamir Medical Center (Assaf Harofeh), Zerifin, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maya","family":"Berlin","sequence":"additional","affiliation":[{"name":"Clinical Pharmacology and Toxicology Unit, Drug Consultation Center, Shamir Medical Center (Assaf Harofeh), Zerifin, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"2023020108515373100_btab769-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-12943-x","article-title":"Development of a machine learning algorithm to classify drugs of unknown fetal effect","volume":"7","author":"Boland","year":"2017","journal-title":"Sci. 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