{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T00:46:50Z","timestamp":1793148410208,"version":"4.5.0"},"reference-count":129,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Phytomedicine"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.phymed.2024.155479","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T18:44:46Z","timestamp":1708713886000},"page":"155479","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":25,"special_numbering":"C","title":["Warfarin\u2013A natural anticoagulant: A review of research trends for precision medication"],"prefix":"10.1016","volume":"128","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4348-6496","authenticated-orcid":false,"given":"Ling","family":"Xue","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3353-7897","authenticated-orcid":false,"given":"Rajeev K.","family":"Singla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6965-6276","authenticated-orcid":false,"given":"Shan","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonia","family":"Arrasate","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Humberto","family":"Gonz\u00e1lez-D\u00edaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyan","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2899-1531","authenticated-orcid":false,"given":"Bairong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.phymed.2024.155479_bib0001","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1007\/s00246-019-02215-y","article-title":"Personalised warfarin dosing in children post-cardiac surgery","volume":"40","author":"Al-Metwali","year":"2019","journal-title":"Pediatr. Cardiol."},{"key":"10.1016\/j.phymed.2024.155479_bib0002","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1111\/jcpt.12143","article-title":"Association of gene polymorphisms with the risk of warfarin bleeding complications at therapeutic INR in patients with mechanical cardiac valves","volume":"39","author":"An","year":"2014","journal-title":"J. Clin. Pharm. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0003","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1161\/CIRCULATIONAHA.107.737312","article-title":"Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation","volume":"116","author":"Anderson","year":"2007","journal-title":"Circulation"},{"key":"10.1016\/j.phymed.2024.155479_bib0006","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1111\/bcp.14608","article-title":"Warfarin dosing algorithms: a systematic review","volume":"87","author":"Asiimwe","year":"2021","journal-title":"Br. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0005","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/psp4.12740","article-title":"Stable warfarin dose prediction in sub-Saharan African patients: a machine-learning approach and external validation of a clinical dose-initiation algorithm","volume":"11","author":"Asiimwe","year":"2022","journal-title":"CPT. Pharmacometrics. Syst. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0007","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1002\/cpt.1796","article-title":"An Introduction to Machine Learning","volume":"107","author":"Badillo","year":"2020","journal-title":"Clin. Pharmacol. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0008","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1017\/S0007114513001013","article-title":"The role of menaquinones (vitamin K(2)) in human health","volume":"110","author":"Beulens","year":"2013","journal-title":"Br. J. Nutr."},{"key":"10.1016\/j.phymed.2024.155479_bib0009","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1021\/acs.molpharmaceut.7b01108","article-title":"Role of hepatic organic anion transporter 2 in the pharmacokinetics of R- and S-warfarin: in vitro studies and mechanistic evaluation","volume":"15","author":"Bi","year":"2018","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.phymed.2024.155479_bib0010","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/j.1742-7843.2009.00488.x","article-title":"Physiologically based modelling and prediction of drug interactions","volume":"106","author":"Bois","year":"2010","journal-title":"Basic Clin. Pharmacol. Toxicol."},{"key":"10.1016\/j.phymed.2024.155479_bib0011","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1111\/j.1368-5031.2006.00790.x","article-title":"Patterns of use of antithrombotic therapy and quality of anticoagulation among patients with non-valvular atrial fibrillation in clinical practice","volume":"60","author":"Boulanger","year":"2006","journal-title":"Int. J. Clin. Pract."},{"key":"10.1016\/j.phymed.2024.155479_bib0012","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2023.1235331","article-title":"Application of a warfarin dosing calculator to guide individualized dosing versus empirical adjustment after fixed dosing: a pilot study","volume":"14","author":"Cai","year":"2023","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0013","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1124\/dmd.114.059311","article-title":"Physiologically based pharmacokinetic modeling to predict drug-drug interactions involving inhibitory metabolite: a case study of amiodarone","volume":"43","author":"Chen","year":"2015","journal-title":"Drug Metab. Dispos."},{"key":"10.1016\/j.phymed.2024.155479_bib0014","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1124\/dmd.122.001048","article-title":"A physiological-based pharmacokinetic model embedded with a target-mediated drug disposition mechanism can characterize single-dose warfarin pharmacokinetic profiles in subjects with various CYP2C9 genotypes under different cotreatments","volume":"51","author":"Cheng","year":"2023","journal-title":"Drug Metab. Dispos."},{"key":"10.1016\/j.phymed.2024.155479_bib128","first-page":"412","article-title":"Using simulation and optimization approach to improve outcome through warfarin precision treatment. Pacific Symposium on Biocomputing","volume":"23","author":"Chi","year":"2018","journal-title":"Pacific Symposium on Biocomputing"},{"key":"10.1016\/j.phymed.2024.155479_bib0015","article-title":"A systematic review of the pharmacokinetic and pharmacodynamic interactions of herbal medicine with warfarin","volume":"12","author":"Choi","year":"2017","journal-title":"PLoS. One"},{"key":"10.1016\/j.phymed.2024.155479_bib129","doi-asserted-by":"crossref","first-page":"22461","DOI":"10.1038\/s41598-023-49831-6","article-title":"Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea","volume":"13","author":"Choi","year":"2023","journal-title":"Scientific reports"},{"key":"10.1016\/j.phymed.2024.155479_bib0016","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s40262-015-0322-y","article-title":"Physiologically-based pharmacokinetic modeling of macitentan: prediction of drug-drug interactions","volume":"55","author":"de Kanter","year":"2016","journal-title":"Clin. Pharmacokinet."},{"key":"10.1016\/j.phymed.2024.155479_bib0017","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"10.1016\/j.phymed.2024.155479_bib0018","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.blre.2017.02.001","article-title":"Old and new oral anticoagulants: food, herbal medicines and drug interactions","volume":"31","author":"Di Minno","year":"2017","journal-title":"Blood Rev."},{"key":"10.1016\/j.phymed.2024.155479_bib0019","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1056\/NEJMoa1300615","article-title":"Dabigatran versus warfarin in patients with mechanical heart valves","volume":"369","author":"Eikelboom","year":"2013","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0020","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1002\/psp4.12903","article-title":"Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models: an application to warfarin","volume":"12","author":"Falkenhagen","year":"2023","journal-title":"CPT. Pharmacometrics. Syst. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0021","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1097\/00007691-199005000-00006","article-title":"Predicting the daily prothrombin time response to warfarin","volume":"12","author":"Farrow","year":"1990","journal-title":"Ther. Drug Monit."},{"key":"10.1016\/j.phymed.2024.155479_bib0022","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/17425255.2019.1604686","article-title":"Pharmacokinetics and pharmacodynamics of oral anticoagulants used in atrial fibrillation","volume":"15","author":"Fawzy","year":"2019","journal-title":"Expert. Opin. Drug Metab. Toxicol."},{"key":"10.1016\/j.phymed.2024.155479_bib0023","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s00228-017-2248-9","article-title":"Assessing the relative potency of (S)- and (R)-warfarin with a new PK-PD model, in relation to VKORC1 genotypes","volume":"73","author":"Ferrari","year":"2017","journal-title":"Eur. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0024","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1002\/psp4.12870","article-title":"Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction","volume":"11","author":"Gill","year":"2022","journal-title":"CPT. Pharmacometrics. Syst. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0025","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13073-017-0495-0","article-title":"Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans","volume":"9","author":"Gottlieb","year":"2017","journal-title":"Genome Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0026","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1177\/0091270004271404","article-title":"Interaction of warfarin with drugs, natural substances, and foods","volume":"45","author":"Greenblatt","year":"2005","journal-title":"J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0027","doi-asserted-by":"crossref","first-page":"29","DOI":"10.2217\/pgs.13.212","article-title":"Prediction of optimal warfarin maintenance dose using advanced artificial neural networks","volume":"15","author":"Grossi","year":"2014","journal-title":"Pharmacogenomics."},{"key":"10.1016\/j.phymed.2024.155479_bib0028","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1007\/s10557-021-07191-1","article-title":"An adapted neural-fuzzy inference system model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in patients after heart valve replacement","volume":"36","author":"Gu","year":"2022","journal-title":"Cardiovasc. Drugs Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0029","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCGEN.119.002602","article-title":"Genotype-guided dosing of warfarin in chinese adults: a multicenter randomized clinical trial","volume":"13","author":"Guo","year":"2020","journal-title":"Circ. Genom. Precis. Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0030","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/sj.clpt.6100084","article-title":"A PK-PD model for predicting the impact of age, CYP2C9, and VKORC1 genotype on individualization of warfarin therapy","volume":"81","author":"Hamberg","year":"2007","journal-title":"Clin. Pharmacol. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0032","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1038\/clpt.2010.37","article-title":"A pharmacometric model describing the relationship between warfarin dose and INR response with respect to variations in CYP2C9, VKORC1, and age","volume":"87","author":"Hamberg","year":"2010","journal-title":"Clin. Pharmacol. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib123","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1007\/s00228-012-1466-4","article-title":"Warfarin dose prediction in children using pharmacometric bridging--comparison with published pharmacogenetic dosing algorithms","volume":"69","author":"Hamberg","year":"2013","journal-title":"Eur. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0031","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s12911-014-0128-0","article-title":"A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children","volume":"15","author":"Hamberg","year":"2015","journal-title":"BMC. Med. Inform. Decis. Mak."},{"key":"10.1016\/j.phymed.2024.155479_bib0033","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1002\/psp4.12111","article-title":"Quantitative systems pharmacology model to predict the effects of commonly used anticoagulants on the human coagulation network","volume":"5","author":"Hartmann","year":"2016","journal-title":"CPT. Pharmacometrics. Syst. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0034","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.3389\/fphar.2020.01041","article-title":"Quantitative systems pharmacology model-based predictions of clinical endpoints to optimize warfarin and rivaroxaban anti-thrombosis therapy","volume":"11","author":"Hartmann","year":"2020","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0035","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s10928-016-9492-y","article-title":"Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine","volume":"43","author":"Hartmanshenn","year":"2016","journal-title":"J. Pharmacokinet. Pharmacodyn."},{"key":"10.1016\/j.phymed.2024.155479_bib0036","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.clinthera.2023.06.002","article-title":"Pharmacokinetic and pharmacodynamic interactions between henagliflozin, a novel selective SGLT-2 inhibitor, and warfarin in healthy Chinese subjects","volume":"45","author":"He","year":"2023","journal-title":"Clin. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0037","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1001\/archinte.165.10.1095","article-title":"Systematic overview of warfarin and its drug and food interactions","volume":"165","author":"Holbrook","year":"2005","journal-title":"Arch. Intern. Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0038","doi-asserted-by":"crossref","first-page":"483","DOI":"10.2165\/00003088-198611060-00005","article-title":"Clinical pharmacokinetics and pharmacodynamics of warfarin. Understanding the dose-effect relationship","volume":"11","author":"Holford","year":"1986","journal-title":"Clin. Pharmacokinet."},{"key":"10.1016\/j.phymed.2024.155479_bib0039","first-page":"287","article-title":"Association between four microRNA binding site-related polymorphisms and the risk of warfarin-induced bleeding complications","volume":"18","author":"Hosseindokht","year":"2019","journal-title":"EXCLI. J."},{"key":"10.1016\/j.phymed.2024.155479_bib0040","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.artmed.2012.04.001","article-title":"Predicting warfarin dosage from clinical data: a supervised learning approach","volume":"56","author":"Hu","year":"2012","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0041","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1002\/cpt.2407","article-title":"Predicting range of initial warfarin dose based on pharmacometabolomic and genetic inputs","volume":"110","author":"Huang","year":"2021","journal-title":"Clin. Pharmacol. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0042","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1056\/NEJMoa0809329","article-title":"Estimation of the warfarin dose with clinical and pharmacogenetic data","volume":"360","author":"International Warfarin Pharmacogenetics","year":"2009","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106548","article-title":"Automated warfarin dose prediction for Asian, American, and Caucasian populations using a deep neural network","volume":"153","author":"Jahmunah","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0044","doi-asserted-by":"crossref","DOI":"10.3390\/pharmaceutics14091814","article-title":"Adoption of machine learning in pharmacometrics: an overview of recent implementations and their considerations","volume":"14","author":"Janssen","year":"2022","journal-title":"Pharmaceutics."},{"key":"10.1016\/j.phymed.2024.155479_bib0045","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1177\/0091270006292124","article-title":"Investigation of the effects of herbal medicines on warfarin response in healthy subjects: a population pharmacokinetic-pharmacodynamic modeling approach","volume":"46","author":"Jiang","year":"2006","journal-title":"J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0046","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1208\/s12248-016-9890-5","article-title":"Development of a physiologically based pharmacokinetic model to predict disease-mediated therapeutic protein-drug interactions: modulation of multiple cytochrome P450 enzymes by interleukin-6","volume":"18","author":"Jiang","year":"2016","journal-title":"AAPS. J."},{"key":"10.1016\/j.phymed.2024.155479_bib0047","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCULATIONAHA.123.066485","article-title":"Safety of switching from a vitamin k antagonist to a non-vitamin k antagonist oral anticoagulant in frail older patients with atrial fibrillation: results of the FRAIL-AF randomized controlled trial","author":"Joosten","year":"2023","journal-title":"Circulation"},{"key":"10.1016\/j.phymed.2024.155479_bib0048","doi-asserted-by":"crossref","first-page":"84","DOI":"10.3945\/ajcn.115.109496","article-title":"Fecal menaquinone profiles of overweight adults are associated with gut microbiota composition during a gut microbiota-targeted dietary intervention","volume":"102","author":"Karl","year":"2015","journal-title":"Am. J. Clin. Nutr."},{"key":"10.1016\/j.phymed.2024.155479_bib0049","doi-asserted-by":"crossref","first-page":"9","DOI":"10.2174\/187152571201141201092129","article-title":"Safety and efficacy of novel oral anticoagulants: a comparison to vitamin K antagonists","volume":"12","author":"Katsianis","year":"2014","journal-title":"Cardiovasc. Hematol. Agents Med. Chem."},{"key":"10.1016\/j.phymed.2024.155479_bib0050","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1002\/phar.1982","article-title":"Warfarin pharmacogenomics in diverse populations","volume":"37","author":"Kaye","year":"2017","journal-title":"Pharmacotherapy."},{"key":"10.1016\/j.phymed.2024.155479_bib0051","doi-asserted-by":"crossref","DOI":"10.3390\/pharmaceutics14081530","article-title":"Machine learning and pharmacometrics for prediction of pharmacokinetic data: differences, similarities and challenges illustrated with rifampicin","volume":"14","author":"Keutzer","year":"2022","journal-title":"Pharmaceutics."},{"key":"10.1016\/j.phymed.2024.155479_bib0052","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/bdd.2178","article-title":"Development of a Korean-specific virtual population for physiologically based pharmacokinetic modelling and simulation","volume":"40","author":"Kim","year":"2019","journal-title":"Biopharm. Drug Dispos."},{"key":"10.1016\/j.phymed.2024.155479_bib0053","doi-asserted-by":"crossref","DOI":"10.3390\/jcm12154984","article-title":"DOACs in the anticoagulation of mechanical valves: a systematic review and future perspectives","volume":"12","author":"Langenaeken","year":"2023","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0054","doi-asserted-by":"crossref","first-page":"14745","DOI":"10.1038\/s41598-021-94305-2","article-title":"Development of a system to support warfarin dose decisions using deep neural networks","volume":"11","author":"Lee","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.phymed.2024.155479_bib0055","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1002\/ajh.23151","article-title":"Accuracy assessment of pharmacogenetic algorithms for warfarin dose prediction in Chinese patients","volume":"87","author":"Lei","year":"2012","journal-title":"Am. J. Hematol."},{"key":"10.1016\/j.phymed.2024.155479_bib0056","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.biopha.2016.06.012","article-title":"Review on mechanisms and interactions in concomitant use of herbs and warfarin therapy","volume":"83","author":"Leite","year":"2016","journal-title":"Biomed. PharmacOther"},{"key":"10.1016\/j.phymed.2024.155479_bib0057","doi-asserted-by":"crossref","first-page":"9712","DOI":"10.1038\/s41598-018-27772-9","article-title":"Warfarin maintenance dose prediction for patients undergoing heart valve replacement- a hybrid model with genetic algorithm and back-propagation neural network","volume":"8","author":"Li","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.phymed.2024.155479_bib0058","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s40261-019-00850-0","article-title":"The prediction model of warfarin individual maintenance dose for patients undergoing heart valve replacement, based on the back propagation neural network","volume":"40","author":"Li","year":"2020","journal-title":"Clin. Drug Investig."},{"key":"10.1016\/j.phymed.2024.155479_bib0059","doi-asserted-by":"crossref","DOI":"10.1145\/2818302","article-title":"Comparison of nine statistical model based warfarin pharmacogenetic dosing algorithms using the racially diverse international warfarin pharmacogenetic consortium cohort database","volume":"10","author":"Liu","year":"2015","journal-title":"PLoS. One"},{"key":"10.1016\/j.phymed.2024.155479_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104242","article-title":"An ensemble learning based framework to estimate warfarin maintenance dose with cross-over variables exploration on incomplete data set","volume":"131","author":"Liu","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0062","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s40262-013-0054-9","article-title":"Prediction of warfarin maintenance dose in Han Chinese patients using a mechanistic model based on genetic and non-genetic factors","volume":"52","author":"Lu","year":"2013","journal-title":"Clin. Pharmacokinet."},{"key":"10.1016\/j.phymed.2024.155479_bib0061","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1002\/bdd.1919","article-title":"Assessment of cytochrome P450-mediated drug-drug interaction potential of orteronel and exposure changes in patients with renal impairment using physiologically based pharmacokinetic modeling and simulation","volume":"35","author":"Lu","year":"2014","journal-title":"Biopharm. Drug Dispos."},{"key":"10.1016\/j.phymed.2024.155479_bib0064","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0205872","article-title":"Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose","volume":"13","author":"Ma","year":"2018","journal-title":"PLoS. One"},{"key":"10.1016\/j.phymed.2024.155479_bib0063","doi-asserted-by":"crossref","first-page":"13778","DOI":"10.1038\/s41598-021-93317-2","article-title":"Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling","volume":"11","author":"Ma","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.phymed.2024.155479_bib0065","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1208\/s12248-019-0309-y","article-title":"Simulating the impact of elevated levels of interleukin-6 on the pharmacokinetics of various CYP450 substrates in patients with neuromyelitis optica or neuromyelitis optica spectrum disorders in different ethnic populations","volume":"21","author":"Machavaram","year":"2019","journal-title":"AAPS. J."},{"key":"10.1016\/j.phymed.2024.155479_bib0066","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106589","article-title":"Determining the adjusted initial treatment dose of warfarin anticoagulant medicine using kernel-based support vector regression","volume":"214","author":"Maghsoudi","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.phymed.2024.155479_bib0067","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1111\/bcp.13186","article-title":"In vitro and physiologically-based pharmacokinetic based assessment of drug-drug interaction potential of canagliflozin","volume":"83","author":"Mamidi","year":"2017","journal-title":"Br. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0068","doi-asserted-by":"crossref","first-page":"644","DOI":"10.4140\/TCP.n.2015.644","article-title":"Comparing direct oral anticoagulants and warfarin for atrial fibrillation, venous thromboembolism, and mechanical heart valves","volume":"30","author":"Marcy","year":"2015","journal-title":"Consult. Pharm."},{"key":"10.1016\/j.phymed.2024.155479_bib0069","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1111\/bcp.14801","article-title":"Machine learning in pharmacometrics: opportunities and challenges","volume":"88","author":"McComb","year":"2022","journal-title":"Br. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0070","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/S0083-6729(07)00013-1","article-title":"Vitamin K and thrombosis","volume":"78","author":"Merli","year":"2008","journal-title":"Vitam. Horm."},{"key":"10.1016\/j.phymed.2024.155479_bib0071","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1046\/j.1365-2125.1998.00792.x","article-title":"Expanding clinical applications of population pharmacodynamic modelling","volume":"46","author":"Minto","year":"1998","journal-title":"Br. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0072","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1111\/jth.15318","article-title":"Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population","volume":"19","author":"Nguyen","year":"2021","journal-title":"J. Thromb. Haemost."},{"key":"10.1016\/j.phymed.2024.155479_bib0073","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1517\/14740338.5.3.433","article-title":"Warfarin and its interactions with foods, herbs and other dietary supplements","volume":"5","author":"Nutescu","year":"2006","journal-title":"Expert. Opin. Drug Saf."},{"key":"10.1016\/j.phymed.2024.155479_bib0074","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1002\/psp4.12643","article-title":"An artificial neural network-pharmacokinetic model and its interpretation using Shapley additive explanations","volume":"10","author":"Ogami","year":"2021","journal-title":"CPT. Pharmacometrics. Syst. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0075","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0105891","article-title":"Determinants of the over-anticoagulation response during warfarin initiation therapy in Asian patients based on population pharmacokinetic-pharmacodynamic analyses","volume":"9","author":"Ohara","year":"2014","journal-title":"PLoS. One"},{"key":"10.1016\/j.phymed.2024.155479_bib0076","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/bdd.2008","article-title":"In silico evaluation of warfarin-bucolome therapy","volume":"37","author":"Ohtani","year":"2016","journal-title":"Biopharm. Drug Dispos."},{"key":"10.1016\/j.phymed.2024.155479_bib0077","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0083-6729(07)00003-9","article-title":"The vitamin K cycle","volume":"78","author":"Oldenburg","year":"2008","journal-title":"Vitam. Horm."},{"key":"10.1016\/j.phymed.2024.155479_bib0078","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1007\/s40262-017-0541-5","article-title":"A joint model for vitamin K-dependent clotting factors and anticoagulation proteins","volume":"56","author":"Ooi","year":"2017","journal-title":"Clin. Pharmacokinet."},{"key":"10.1016\/j.phymed.2024.155479_bib0079","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1080\/10408398009527294","article-title":"Determination of vitamin K in foods: a review","volume":"13","author":"Parrish","year":"1980","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"10.1016\/j.phymed.2024.155479_bib0080","doi-asserted-by":"crossref","first-page":"121","DOI":"10.2217\/pgs.15.161","article-title":"Artificial neural network-based pharmacogenomic algorithm for warfarin dose optimization","volume":"17","author":"Pavani","year":"2016","journal-title":"Pharmacogenomics."},{"key":"10.1016\/j.phymed.2024.155479_bib0081","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.2217\/pgs-2016-0058","article-title":"Development of neuro-fuzzy model to explore gene-nutrient interactions modulating warfarin dose requirement","volume":"17","author":"Pavani","year":"2016","journal-title":"Pharmacogenomics."},{"key":"10.1016\/j.phymed.2024.155479_bib132","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1160\/TH11-08-0568","article-title":"The Creating an Optimal Warfarin Nomogram (CROWN) Study","volume":"107","author":"Perlstein","year":"2012","journal-title":"Thrombosis and haemostasis"},{"key":"10.1016\/j.phymed.2024.155479_bib0082","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1111\/j.1365-2125.2006.02806.x","article-title":"Warfarin: almost 60 years old and still causing problems","volume":"62","author":"Pirmohamed","year":"2006","journal-title":"Br. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0083","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1097\/00007691-200302000-00005","article-title":"A Bayesian method based on clotting factor activity for the prediction of maintenance warfarin dosage regimens","volume":"25","author":"Pitsiu","year":"2003","journal-title":"Ther. Drug Monit."},{"key":"10.1016\/j.phymed.2024.155479_bib127","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCGENETICS.117.001804","article-title":"Personalized Anticoagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials","volume":"10","author":"Ravvaz","year":"2017","journal-title":"Circ. Cardiovasc. Genet."},{"key":"10.1016\/j.phymed.2024.155479_bib0084","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1097\/FTD.0000000000000347","article-title":"Influence of genotype on warfarin maintenance dose predictions produced using a bayesian dose individualization tool","volume":"38","author":"Saffian","year":"2016","journal-title":"Ther. Drug Monit."},{"key":"10.1016\/j.phymed.2024.155479_bib0085","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1124\/dmd.115.065920","article-title":"Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification","volume":"43","author":"Sager","year":"2015","journal-title":"Drug Metab. Dispos."},{"key":"10.1016\/j.phymed.2024.155479_bib0086","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s40291-014-0090-7","article-title":"Dosage individualization of warfarin using artificial neural networks","volume":"18","author":"Saleh","year":"2014","journal-title":"Mol. Diagn. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0087","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1097\/FPC.0b013e3283333b80","article-title":"Pharmacogenomic trial design: use of a PK\/PD model to explore warfarin dosing interventions through clinical trial simulation","volume":"19","author":"Salinger","year":"2009","journal-title":"Pharmacogenet. Genomics."},{"key":"10.1016\/j.phymed.2024.155479_bib121","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.2217\/pgs.09.65","article-title":"Warfarin-dosing algorithm based on a population pharmacokinetic\/pharmacodynamic model combined with Bayesian forecasting","volume":"10","author":"Sasaki","year":"2009","journal-title":"Pharmacogenomics"},{"key":"10.1016\/j.phymed.2024.155479_bib125","doi-asserted-by":"crossref","DOI":"10.1155\/2015\/560108","article-title":"Revisiting Warfarin Dosing Using Machine Learning Techniques","author":"Sharabiani","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0088","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s10928-021-09757-w","article-title":"Fast screening of covariates in population models empowered by machine learning","volume":"48","author":"Sibieude","year":"2021","journal-title":"J. Pharmacokinet. Pharmacodyn."},{"key":"10.1016\/j.phymed.2024.155479_bib0089","article-title":"Fermented formulation of silybum marianum seeds: optimization, heavy metal analysis, and hepatoprotective assessment","author":"Singla","year":"2023","journal-title":"Phytomedicine"},{"key":"10.1016\/j.phymed.2024.155479_bib0090","article-title":"Bibliometric and temporal trend analysis of nipah virus- an emerging zoonotic disease: what do we know so far","author":"Singla","year":"2023","journal-title":"bioRxiv."},{"key":"10.1016\/j.phymed.2024.155479_bib0091","first-page":"732","article-title":"Applying an artificial neural network to warfarin maintenance dose prediction","volume":"6","author":"Solomon","year":"2004","journal-title":"Isr. Med. Assoc. J."},{"key":"10.1016\/j.phymed.2024.155479_bib0092","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11096-022-01471-y","article-title":"Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin","volume":"45","author":"Sridharan","year":"2023","journal-title":"Int. J. Clin. Pharm."},{"key":"10.1016\/j.phymed.2024.155479_bib0093","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/S0003-9993(95)80577-X","article-title":"Initialization of warfarin dosages using computer modeling","volume":"76","author":"Sun","year":"1995","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"10.1016\/j.phymed.2024.155479_bib0094","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1097\/00007691-198506000-00006","article-title":"Bayesian pharmacokinetic\/pharmacodynamic forecasting of prothrombin response to warfarin therapy: preliminary evaluation","volume":"7","author":"Svec","year":"1985","journal-title":"Ther. Drug Monit."},{"key":"10.1016\/j.phymed.2024.155479_bib133","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1097\/FPC.0000000000000165","article-title":"Pharmacogenetic versus clinical dosing of warfarin in individuals of Chinese and African-American ancestry: assessment using data simulation","volume":"25","author":"Syn","year":"2015","journal-title":"Pharmacogenetics and genomics"},{"key":"10.1016\/j.phymed.2024.155479_bib0095","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1111\/bcp.14404","article-title":"Warfarin and food, herbal or dietary supplement interactions: a systematic review","volume":"87","author":"Tan","year":"2021","journal-title":"Br. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0096","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s12893-018-0343-1","article-title":"A prediction study of warfarin individual stable dose after mechanical heart valve replacement: adaptive neural-fuzzy inference system prediction","volume":"18","author":"Tao","year":"2018","journal-title":"BMC. Surg."},{"key":"10.1016\/j.phymed.2024.155479_bib0097","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1109\/JBHI.2018.2812165","article-title":"Evolutionary ensemble learning algorithm to modeling of warfarin dose prediction for Chinese","volume":"23","author":"Tao","year":"2019","journal-title":"IEEe J. Biomed. Health Inform."},{"key":"10.1016\/j.phymed.2024.155479_bib0098","doi-asserted-by":"crossref","first-page":"2642","DOI":"10.1109\/JBHI.2019.2891164","article-title":"An ensemble model with clustering assumption for warfarin dose prediction in Chinese patients","volume":"23","author":"Tao","year":"2019","journal-title":"IEEe J. Biomed. Health Inform."},{"key":"10.1016\/j.phymed.2024.155479_bib0099","first-page":"164","article-title":"Consensus of Chinese experts on anticoagulant therapy in cardiac valve surgery","volume":"38","author":"The Group of Valve Surgery","year":"2022","journal-title":"Chin J Thorac Cardiovasc Surg"},{"key":"10.1016\/j.phymed.2024.155479_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2020.103634","article-title":"Evaluating warfarin dosing models on multiple datasets with a novel software framework and evolutionary optimisation","volume":"113","author":"Truda","year":"2021","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.phymed.2024.155479_bib0101","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.2165\/00003088-200544120-00003","article-title":"Comparative pharmacokinetics of vitamin K antagonists: warfarin, phenprocoumon and acenocoumarol","volume":"44","author":"Ufer","year":"2005","journal-title":"Clin. Pharmacokinet."},{"key":"10.1016\/j.phymed.2024.155479_bib131","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1046\/j.1365-2125.1999.00967.x","article-title":"Prediction of the international normalized ratio and maintenance dose during the initiation of warfarin therapy","volume":"48","author":"Vadher","year":"1999","journal-title":"British journal of clinical pharmacology"},{"key":"10.1016\/j.phymed.2024.155479_bib0102","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/sj.tpj.6500417","article-title":"Pharmacogenetics of warfarin: current status and future challenges","volume":"7","author":"Wadelius","year":"2007","journal-title":"Pharmacogenomics. J."},{"key":"10.1016\/j.phymed.2024.155479_bib0103","doi-asserted-by":"crossref","first-page":"e191","DOI":"10.1161\/CIRCULATIONAHA.114.010426","article-title":"Cardiology patient page. Warfarin versus novel oral anticoagulants: how to choose?","volume":"130","author":"Wadhera","year":"2014","journal-title":"Circulation"},{"key":"10.1016\/j.phymed.2024.155479_bib0104","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1038\/clpt.2009.87","article-title":"A comprehensive model for the humoral coagulation network in humans","volume":"86","author":"Wajima","year":"2009","journal-title":"Clin. Pharmacol. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0107","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1002\/cpt.2687","article-title":"Physiologically-based pharmacokinetic modeling-guided dose management of oral anticoagulants when initiating nirmatrelvir\/ritonavir (Paxlovid) for COVID-19 treatment","volume":"112","author":"Wang","year":"2022","journal-title":"Clin. Pharmacol. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0108","doi-asserted-by":"crossref","DOI":"10.1016\/j.dmpk.2020.10.001","article-title":"A physiologically based pharmacokinetic\/pharmacodynamic modeling approach for drug-drug interaction evaluation of warfarin enantiomers with sorafenib","volume":"39","author":"Wang","year":"2021","journal-title":"Drug Metab. Pharmacokinet."},{"key":"10.1016\/j.phymed.2024.155479_bib0106","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1111\/cts.13164","article-title":"Utilization of physiologically-based pharmacokinetic model to assess disease-mediated therapeutic protein-disease-drug interaction in immune-mediated inflammatory diseases","volume":"15","author":"Wang","year":"2022","journal-title":"Clin. Transl. Sci."},{"key":"10.1016\/j.phymed.2024.155479_bib0105","article-title":"Opuntia ficus-indica (L.) Mill. - anticancer properties and phytochemicals: current trends and future perspectives","volume":"14","author":"Wang","year":"2023","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.phymed.2024.155479_bib0109","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1111\/j.1365-2141.2008.07119.x","article-title":"The story of the discovery of heparin and warfarin","volume":"141","author":"Wardrop","year":"2008","journal-title":"Br. J. Haematol."},{"key":"10.1016\/j.phymed.2024.155479_bib0110","doi-asserted-by":"crossref","first-page":"676","DOI":"10.7326\/0003-4819-121-9-199411010-00009","article-title":"Interactions of warfarin with drugs and food","volume":"121","author":"Wells","year":"1994","journal-title":"Ann. Intern. Med."},{"key":"10.1016\/j.phymed.2024.155479_bib0111","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1007\/s11095-011-0369-x","article-title":"Development of a bayesian forecasting method for warfarin dose individualization","volume":"28","author":"Wright","year":"2011","journal-title":"Pharm. Res."},{"key":"10.1016\/j.phymed.2024.155479_bib0112","doi-asserted-by":"crossref","first-page":"23","DOI":"10.2217\/pgs-2019-0124","article-title":"Comparison of the prediction performance of different warfarin dosing algorithms based on Chinese patients","volume":"21","author":"Xie","year":"2020","journal-title":"Pharmacogenomics."},{"key":"10.1016\/j.phymed.2024.155479_bib0113","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1111\/bcp.13157","article-title":"Theory-based pharmacokinetics and pharmacodynamics of S- and R-warfarin and effects on international normalized ratio: influence of body size, composition and genotype in cardiac surgery patients","volume":"83","author":"Xue","year":"2017","journal-title":"Br. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0114","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s00228-019-02655-8","article-title":"Relationship between warfarin dosage and international normalized ratio: a dose-response analysis and evaluation based on multicenter data","volume":"75","author":"Xue","year":"2019","journal-title":"Eur. J. Clin. Pharmacol."},{"key":"10.1016\/j.phymed.2024.155479_bib0115","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1038\/s41431-019-0450-1","article-title":"APOB gene polymorphisms may affect the risk of minor or minimal bleeding complications in patients on warfarin maintaining therapeutic INR","volume":"27","author":"Yee","year":"2019","journal-title":"Eur. J. Hum. Genet."},{"key":"10.1016\/j.phymed.2024.155479_bib0116","doi-asserted-by":"crossref","first-page":"659","DOI":"10.2217\/pgs-2019-0005","article-title":"Association between VEGFA gene polymorphisms and bleeding complications in patients maintaining therapeutic international normalized ratio","volume":"20","author":"Yee","year":"2019","journal-title":"Pharmacogenomics."},{"key":"10.1016\/j.phymed.2024.155479_bib0117","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.2147\/DDDT.S198018","article-title":"Genetic variations in the transcription factors GATA4 and GATA6 and bleeding complications in patients receiving warfarin therapy","volume":"13","author":"Yee","year":"2019","journal-title":"Drug Des. Devel. Ther."},{"key":"10.1016\/j.phymed.2024.155479_bib0118","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10928-009-9138-4","article-title":"Ethnic differences in the population pharmacokinetics and pharmacodynamics of warfarin","volume":"37","author":"Yuen","year":"2010","journal-title":"J. Pharmacokinet. Pharmacodyn."},{"key":"10.1016\/j.phymed.2024.155479_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2022.104267","article-title":"Optimizing warfarin dosing using deep reinforcement learning","volume":"137","author":"Anzabi Zadeh","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.phymed.2024.155479_bib0119","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1093\/jamia\/ocac088","article-title":"Optimizing the dynamic treatment regime of in-hospital warfarin anticoagulation in patients after surgical valve replacement using reinforcement learning","volume":"29","author":"Zeng","year":"2022","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.phymed.2024.155479_bib0120","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1016\/j.ijcard.2014.08.062","article-title":"Use of artificial neural network to predict warfarin individualized dosage regime in Chinese patients receiving low-intensity anticoagulation after heart valve replacement","volume":"176","author":"Zhou","year":"2014","journal-title":"Int. J. Cardiol."}],"container-title":["Phytomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0944711324001430?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0944711324001430?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T15:20:36Z","timestamp":1724080836000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0944711324001430"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":129,"alternative-id":["S0944711324001430"],"URL":"https:\/\/doi.org\/10.1016\/j.phymed.2024.155479","relation":{},"ISSN":["0944-7113"],"issn-type":[{"value":"0944-7113","type":"print"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Warfarin\u2013A natural anticoagulant: A review of research trends for precision medication","name":"articletitle","label":"Article Title"},{"value":"Phytomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.phymed.2024.155479","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier GmbH. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"155479"}}