{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T06:04:01Z","timestamp":1789365841574,"version":"build-2803163510"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Adverse drug reactions (ADRs) are a leading cause of preventable hospitalization, yet the dominant pharmacovigilance paradigm remains reactive. Machine learning (ML) offers a data-driven alternative, but the evidence base has not been formally meta-analyzed under clinically realistic inclusion criteria and a dependence-respecting statistical framework. This is the first PRISMA 2020-compliant and PROBAST-screened multilevel meta-analysis of broad-spectrum ML-based ADR prediction. We aimed to quantify pooled discrimination of ML models for broad-spectrum ADR prediction and to test the influence of algorithm class, protein-target features, and outcome breadth.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 compliant, PROSPERO-registered protocol (CRD420250653686), we searched PubMed and Web of Science Core Collection from inception to 30 January 2025. A Scopus and IEEE Xplore top-up search on 2 February 2026 yielded no further records. Eligible studies applied ML to clinically validated data sources for multi-drug ADR prediction. Areas under the receiver operating characteristic curve (AUCs) were logit-transformed and pooled with a study-level DerSimonian-Laird random-effects model and a pre-specified primary three-level multilevel meta-regression (restricted maximum likelihood [REML]) using 30 model-level AUCs nested within 9 contributing studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Eleven Prediction model Risk Of Bias ASsessment Tool (PROBAST) screened studies were included, of which 9 contributed quantitatively. The pre-specified primary three-level multilevel pooled AUC was 0.841 (95% confidence interval [CI] 0.789-0.883), with a study-level DerSimonian-Laird sensitivity estimate of 0.832 (95% CI 0.788-0.869, I-squared 90.7%). Leave-one-study-out estimates ranged 0.818-0.842. Neural networks did not statistically outperform logistic regression, random forests, or k-nearest neighbor. An apparent protein-target feature penalty (model-level p = 0.0002) was driven by one dominant study and disappeared in multilevel sensitivity analysis (adjusted p = 0.75).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML models showed moderate discrimination under Grading of Recommendations Assessment, Development and Evaluation (GRADE) low certainty. Within this small and methodologically heterogeneous evidence base, the pooled AUC is a descriptive summary and does not establish broadly generalizable performance across data sources, ADR definitions, feature-engineering strategies, or validation settings. The high heterogeneity indicates substantial variation in underlying performance across contexts. Standardized benchmarks, harmonized outcome taxonomies, mandatory external validation, and regulator-aligned prospective evaluation are prerequisites for clinical deployment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic Review Registration<\/jats:title>\n                    <jats:p>\n                      [\n                      <jats:ext-link>https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD420250653686<\/jats:ext-link>\n                      ], identifier [CRD420250653686].\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2026.1906920","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:39:32Z","timestamp":1789364372000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning models for adverse drug reaction prediction. A systematic review and three-level multilevel meta-analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Giorgio","family":"Cappello","sequence":"first","affiliation":[{"name":"Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Division of Clinical Neurooncology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Oster","sequence":"additional","affiliation":[{"name":"Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Division of Clinical Neurooncology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]},{"name":"German Cancer Consortium (DKTK), Partner Site Essen\/Dusseldorf, a Partnership Between DKFZ and University Hospital Essen, DKFZ-Division Translational Neurooncology at the West German Cancer Center (WTZ), University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teresa","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Division of Clinical Neurooncology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jana","family":"Grieger","sequence":"additional","affiliation":[{"name":"Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Division of Clinical Neurooncology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leon","family":"Jekel","sequence":"additional","affiliation":[{"name":"Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Division of Clinical Neurooncology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathrin","family":"Kizina","sequence":"additional","affiliation":[{"name":"Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Division of Clinical Neurooncology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yahya","family":"Ahmadipour","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery and Spine Surgery, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurel","family":"Rauschenbach","sequence":"additional","affiliation":[{"name":"German Cancer Consortium (DKTK), Partner Site Essen\/Dusseldorf, a Partnership Between DKFZ and University Hospital Essen, DKFZ-Division Translational Neurooncology at the West German Cancer Center (WTZ), University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]},{"name":"Department of Neurosurgery and Spine Surgery, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Dammann","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery and Spine Surgery, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulrich","family":"Sure","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery and Spine Surgery, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathy","family":"Keyvani","sequence":"additional","affiliation":[{"name":"Institute of Neuropathology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Junker","sequence":"additional","affiliation":[{"name":"Institute of Neuropathology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lazaros","family":"Lazaridis","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Hospital Knappschaftskrankenhaus Bochum, Ruhr University Bochum","place":["Bochum, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Stuschke","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"P\u00f6ttgen","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bj\u00f6rn","family":"Scheffler","sequence":"additional","affiliation":[{"name":"German Cancer Consortium (DKTK), Partner Site Essen\/Dusseldorf, a Partnership Between DKFZ and University Hospital Essen, DKFZ-Division Translational Neurooncology at the West German Cancer Center (WTZ), University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]},{"name":"German Cancer Research Center (DKFZ)","place":["Heidelberg, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Kleinschnitz","sequence":"additional","affiliation":[{"name":"Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Division of Clinical Neurooncology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sied","family":"Kebir","sequence":"additional","affiliation":[{"name":"Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Division of Clinical Neurooncology, University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]},{"name":"German Cancer Consortium (DKTK), Partner Site Essen\/Dusseldorf, a Partnership Between DKFZ and University Hospital Essen, DKFZ-Division Translational Neurooncology at the West German Cancer Center (WTZ), University Medicine Essen, University Duisburg-Essen","place":["Essen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1001\/jama.2017.12126","article-title":"Discrimination and calibration of clinical prediction models, users' guides to the medical literature","volume":"318","author":"Alba","year":"2017","journal-title":"JAMA"},{"key":"B2","doi-asserted-by":"publisher","first-page":"137","DOI":"10.2131\/jts.45.137","article-title":"In silico systems for predicting chemical-induced side effects using known and potential chemical protein interactions, enabling mechanism estimation","volume":"45","author":"Amano","year":"2020","journal-title":"J. Toxicol. Sci."},{"key":"B3","doi-asserted-by":"publisher","first-page":"154","DOI":"10.20982\/tqmp.12.3.p154","article-title":"Fitting three-level meta-analytic models in R, a step-by-step tutorial","volume":"12","author":"Assink","year":"2016","journal-title":"Quant. Methods Psychol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1109\/TCBB.2022.3175362","article-title":"Modular multi-source prediction of drug side-effects with DruGNN","volume":"20","author":"Bongini","year":"2023","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1007\/s11095-024-03742-x","article-title":"Machine learning prediction of on\/off target-driven clinical adverse events","volume":"41","author":"Cao","year":"2024","journal-title":"Pharm. Res."},{"key":"B6","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","year":"2019","journal-title":"J. Clin. Epidemiol."},{"key":"B7","doi-asserted-by":"publisher","first-page":"g7594","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD), the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ"},{"key":"B8","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement, updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"B9","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/0197-2456(86)90046-2","article-title":"Meta-analysis in clinical trials","volume":"7","author":"DerSimonian","year":"1986","journal-title":"Control Clin. Trials"},{"key":"B10","doi-asserted-by":"publisher","first-page":"e70028","DOI":"10.1002\/pds.70028","article-title":"The FDA sentinel real world evidence data enterprise (RWE-DE)","volume":"33","author":"Desai","year":"2024","journal-title":"Pharmacoepidemiol Drug Saf."},{"key":"B11","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1186\/s12859-018-2544-0","article-title":"Predicting adverse drug reactions through interpretable deep learning framework","volume":"19","author":"Dey","year":"2018","journal-title":"BMC Bioinforma."},{"key":"B12","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.sapharm.2025.02.008","article-title":"Artificial intelligence (AI) in pharmacovigilance, a systematic review on predicting adverse drug reactions (ADR) in hospitalized patients","volume":"21","author":"Dsouza","year":"2025","journal-title":"Res. Soc. Adm. Pharm."},{"key":"B13","doi-asserted-by":"publisher","first-page":"204","DOI":"10.14569\/IJACSA.2022.0130924","article-title":"A comprehensive review and application of interpretable deep learning model for ADR prediction","volume":"13","author":"Dubey","year":"2022","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"B14","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.jclinepi.2019.12.023","article-title":"GRADE guidelines 28, use of GRADE for the assessment of evidence about prognostic factors","volume":"121","author":"Foroutan","year":"2020","journal-title":"J. Clin. Epidemiol."},{"key":"B15","doi-asserted-by":"publisher","first-page":"4575","DOI":"10.1038\/s41467-020-18305-y","article-title":"Predicting the frequencies of drug side effects","volume":"11","author":"Galeano","year":"2020","journal-title":"Nat. Commun."},{"key":"B16","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"B17","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.clinthera.2024.06.003","article-title":"Knowledge graphs in pharmacovigilance: a scoping review","volume":"46","author":"Hauben","year":"2024","journal-title":"Clin. Ther."},{"key":"B18","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1136\/bmj.327.7414.557","article-title":"Measuring inconsistency in meta-analyses","volume":"327","author":"Higgins","year":"2003","journal-title":"BMJ"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1497397","DOI":"10.3389\/fphar.2024.1497397","article-title":"Predicting adverse drug event using machine learning based on electronic health records, a systematic review and meta-analysis","volume":"15","author":"Hu","year":"2024","journal-title":"Front. Pharmacol."},{"key":"B20","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1038\/s41598-017-00908-z","article-title":"Predicting neurological adverse drug reactions based on biological, chemical and phenotypic properties of drugs using machine learning models","volume":"7","author":"Jamal","year":"2017","journal-title":"Sci. Rep."},{"key":"B21","doi-asserted-by":"publisher","first-page":"e29387","DOI":"10.1097\/MD.0000000000029387","article-title":"Analyzing adverse drug reaction using statistical and machine learning methods: a systematic review","volume":"101","author":"Kim","year":"2022","journal-title":"Med. Baltim."},{"key":"B22","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/UBMK67458.2025.11206959","article-title":"Predicting drug-drug interaction side effects using machine learning: a comparative study of homogeneous and graph-based approaches","volume-title":"2025 10th International Conference on Computer Science and Engineering (UBMK)","author":"Ko\u00e7","year":"2025"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1345\/aph.1L037","article-title":"Hospital admissions associated with adverse drug reactions: a systematic review of prospective observational studies","volume":"42","author":"Kongkaew","year":"2008","journal-title":"Ann. Pharmacother."},{"key":"B24","doi-asserted-by":"publisher","first-page":"1884","DOI":"10.1093\/bib\/bbaa040","article-title":"Prediction of drug adverse events using deep learning in pharmaceutical discovery","volume":"22","author":"Lee","year":"2021","journal-title":"Brief. Bioinform"},{"key":"B25","doi-asserted-by":"publisher","first-page":"e28","DOI":"10.1136\/amiajnl-2011-000699","article-title":"Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs","volume":"19","author":"Liu","year":"2012","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"B26","doi-asserted-by":"publisher","first-page":"2050046","DOI":"10.1142\/S0219720020500468","article-title":"Prediction of adverse drug reactions using drug convolutional neural networks","volume":"19","author":"Mantripragada","year":"2021","journal-title":"J. Bioinform Comput. Biol."},{"key":"B27","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M18-1377","article-title":"PROBAST, a tool to assess risk of bias and applicability of prediction model studies. Explanation and elaboration","volume":"170","author":"Moons","year":"2019","journal-title":"Ann. Intern Med."},{"key":"B28","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1093\/bib\/bbx099","article-title":"Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models","volume":"20","author":"Munoz","year":"2019","journal-title":"Brief. Bioinform"},{"key":"B29","doi-asserted-by":"publisher","first-page":"e055551","DOI":"10.1136\/bmjopen-2021-055551","article-title":"Adverse drug reactions, multimorbidity and polypharmacy: a prospective analysis of 1 month of medical admissions","volume":"12","author":"Osanlou","year":"2022","journal-title":"BMJ Open"},{"key":"B30","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement, an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"B31","doi-asserted-by":"publisher","first-page":"1308","DOI":"10.1002\/pds.5524","article-title":"Artificial intelligence in pharmacovigilance, a regulatory perspective on explainability","volume":"31","author":"Pinheiro","year":"2022","journal-title":"Pharmacoepidemiol Drug Saf."},{"key":"B32","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"B33","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat. Med."},{"key":"B34","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13643-020-01542-z","article-title":"PRISMA-S, an extension to the PRISMA statement for reporting literature searches in systematic reviews","volume":"10","author":"Rethlefsen","year":"2021","journal-title":"Syst. Rev."},{"key":"B35","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s12911-016-0265-8","article-title":"How to promote adverse drug reaction reports using information systems, a systematic review and meta-analysis","volume":"16","author":"Ribeiro-Vaz","year":"2016","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"B36","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/1472-6947-7-16","article-title":"Utilization of the PICO framework to improve searching PubMed for clinical questions","volume":"7","author":"Schardt","year":"2007","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"B37","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1002\/jrsm.1078","article-title":"Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews","volume":"4","author":"Schunemann","year":"2013","journal-title":"Res. Synth. Methods"},{"key":"B38","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1177\/2042098613490780","article-title":"Monitoring product safety in the postmarketing environment","volume":"4","author":"Sharrar","year":"2013","journal-title":"Ther. Adv. Drug Saf."},{"key":"B39","doi-asserted-by":"publisher","first-page":"576","DOI":"10.3758\/s13428-012-0261-6","article-title":"Three-level meta-analysis of dependent effect sizes","volume":"45","author":"Van den Noortgate","year":"2013","journal-title":"Behav. Res. Methods"},{"key":"B40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i03","article-title":"Conducting meta-analyses in R with the metafor package","volume":"36","author":"Viechtbauer","year":"2010","journal-title":"J. Stat. Softw."},{"key":"B41","doi-asserted-by":"publisher","first-page":"e11016","DOI":"10.2196\/11016","article-title":"Detecting potential adverse drug reactions using a deep neural network model","volume":"21","author":"Wang","year":"2019","journal-title":"J. Med. Internet Res."},{"key":"B42","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST, a tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann. Intern Med."},{"key":"B43","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1093\/jamia\/ocad014","article-title":"Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis, a systematic review","volume":"30","author":"Yasrebi-de Kom","year":"2023","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"B44","doi-asserted-by":"publisher","first-page":"bbab580","DOI":"10.1093\/bib\/bbab580","article-title":"ADENet, a novel network-based inference method for prediction of drug adverse events","volume":"23","author":"Yu","year":"2022","journal-title":"Brief. Bioinform"},{"key":"B45","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s12911-021-01402-3","article-title":"Prediction of adverse drug reactions based on knowledge graph embedding","volume":"21","author":"Zhang","year":"2021","journal-title":"BMC Med. Inf. Decis. Mak."}],"container-title":["Frontiers in Pharmacology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphar.2026.1906920\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:39:33Z","timestamp":1789364373000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphar.2026.1906920\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,14]]},"references-count":45,"alternative-id":["10.3389\/fphar.2026.1906920"],"URL":"https:\/\/doi.org\/10.3389\/fphar.2026.1906920","relation":{},"ISSN":["1663-9812"],"issn-type":[{"value":"1663-9812","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,14]]},"article-number":"1906920"}}