{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T23:49:59Z","timestamp":1790293799544,"version":"4.1.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["164134\/2022-0"],"award-info":[{"award-number":["164134\/2022-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2016\/14172-6"],"award-info":[{"award-number":["2016\/14172-6"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["08\/57860-3"],"award-info":[{"award-number":["08\/57860-3"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Thromb Thrombolysis"],"DOI":"10.1007\/s11239-025-03222-y","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:44:54Z","timestamp":1765961094000},"page":"727-745","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial intelligence in computational modeling of thrombosis: Bridging mechanistic insights and clinical translation"],"prefix":"10.1007","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2491-7297","authenticated-orcid":false,"given":"Mohamad Al","family":"Bannoud","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiago Dias","family":"Martins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silmara Aparecida de Lima","family":"Montalv\u00e3o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joyce Maria","family":"Annichino-Bizzacchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rubens Maciel","family":"Filho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Regina Wolf","family":"Maciel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"3222_CR1","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1182\/bloodadvances.2020003576","volume":"6","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Begum HA, Grewal H et al (2022) Cost-effectiveness of diagnostic strategies for venous thromboembolism: a systematic review. Blood Adv 6:544\u2013567. https:\/\/doi.org\/10.1182\/bloodadvances.2020003576","journal-title":"Blood Adv"},{"key":"3222_CR2","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.122.027514","volume":"11","author":"IT Farmakis","year":"2022","unstructured":"Farmakis IT, Barco S, Mavromanoli AC et al (2022) Cost-of\u2010illness analysis of long\u2010term health care resource use and disease burden in patients with pulmonary embolism: insights from the PREFER in VTE registry. J Am Heart Assoc 11:e027514. https:\/\/doi.org\/10.1161\/JAHA.122.027514","journal-title":"J Am Heart Assoc"},{"key":"3222_CR3","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1024\/0301-1526\/a000946","volume":"50","author":"A Vison\u00e0","year":"2021","unstructured":"Vison\u00e0 A, Quere I, Mazzolai L et al (2021) Post-thrombotic syndrome. Vasa 50:331\u2013340. https:\/\/doi.org\/10.1024\/0301-1526\/a000946","journal-title":"Vasa"},{"key":"3222_CR4","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1093\/ehjqcco\/qcz035","volume":"6","author":"N Gustafsson","year":"2020","unstructured":"Gustafsson N, Poulsen PB, Stallknecht SE et al (2020) Societal costs of venous thromboembolism and subsequent major bleeding events: a national register-based study. European Heart Journal - Quality of Care and Clinical Outcomes 6:130\u2013137. https:\/\/doi.org\/10.1093\/ehjqcco\/qcz035","journal-title":"European Heart Journal - Quality of Care and Clinical Outcomes"},{"key":"3222_CR5","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2020.01231","author":"A Mela","year":"2020","unstructured":"Mela A, Rdzanek E, Poniatowski \u0141A et al (2020) Economic costs of cardiovascular diseases in Poland estimates for 2015\u20132017 years. Front Pharmacol. https:\/\/doi.org\/10.3389\/fphar.2020.01231","journal-title":"Front Pharmacol"},{"key":"3222_CR6","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1111\/jth.15788","volume":"20","author":"L Genge","year":"2022","unstructured":"Genge L, Krala A, Tritschler T et al (2022) Evaluation of patients\u2019 experience and related qualitative outcomes in venous thromboembolism: a scoping review. J Thromb Haemost 20:2323\u20132341. https:\/\/doi.org\/10.1111\/jth.15788","journal-title":"J Thromb Haemost"},{"key":"3222_CR7","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s10439-023-03390-z","volume":"52","author":"C Watson","year":"2024","unstructured":"Watson C, Saaid H, Vedula V et al (2024) Venous thromboembolism: review of clinical challenges, biology, assessment, treatment, and modeling. Ann Biomed Eng 52:467\u2013486. https:\/\/doi.org\/10.1007\/s10439-023-03390-z","journal-title":"Ann Biomed Eng"},{"key":"3222_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/ijms24043169","author":"D Pastori","year":"2023","unstructured":"Pastori D, Cormaci VM, Marucci S et al (2023) A comprehensive review of risk factors for venous thromboembolism: from epidemiology to pathophysiology. Int J Mol Sci. https:\/\/doi.org\/10.3390\/ijms24043169","journal-title":"Int J Mol Sci"},{"key":"3222_CR9","doi-asserted-by":"publisher","DOI":"10.1097\/FTD.0000000000000625","author":"T Preijers","year":"2019","unstructured":"Preijers T, Sch\u00fctte LM, Kruip MJHA et al (2019) Strategies for individualized dosing of clotting factor concentrates and desmopressin in hemophilia A and B. Ther Drug Monit. https:\/\/doi.org\/10.1097\/FTD.0000000000000625","journal-title":"Ther Drug Monit"},{"key":"3222_CR10","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/s11239-015-1310-7","volume":"41","author":"AE Burnett","year":"2016","unstructured":"Burnett AE, Mahan CE, Vazquez SR et al (2016) Guidance for the practical management of the direct oral anticoagulants (DOACs) in VTE treatment. J Thromb Thrombolysis 41:206\u2013232. https:\/\/doi.org\/10.1007\/s11239-015-1310-7","journal-title":"J Thromb Thrombolysis"},{"key":"3222_CR11","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1055\/s-0041-1722862","volume":"47","author":"J Arruda","year":"2021","unstructured":"Arruda J, Samelson-Jones BJVRW (2021) Gene therapy for inherited bleeding disorders. Semin Thromb Hemost 47:161\u2013173. https:\/\/doi.org\/10.1055\/s-0041-1722862","journal-title":"Semin Thromb Hemost"},{"key":"3222_CR12","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1016\/j.jtha.2022.12.018","volume":"21","author":"C Dekimpe","year":"2023","unstructured":"Dekimpe C, Roose E, Sakai K et al (2023) Toward gene therapy for congenital thrombotic thrombocytopenic purpura. J Thromb Haemost 21:1090\u20131099. https:\/\/doi.org\/10.1016\/j.jtha.2022.12.018","journal-title":"J Thromb Haemost"},{"key":"3222_CR13","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1111\/joim.13650","volume":"294","author":"JL Gerber","year":"2023","unstructured":"Gerber JL, Messmer AS, Krebs T et al (2023) Utility and limitations of patient-adjusted D-dimer cut-off levels for diagnosis of venous thromboembolism\u2014a systematic review and meta-analysis. J Intern Med 294:110\u2013120. https:\/\/doi.org\/10.1111\/joim.13650","journal-title":"J Intern Med"},{"key":"3222_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15050577","author":"F Corvino","year":"2025","unstructured":"Corvino F, Giurazza F, Galia M et al (2025) Intravascular ultrasound findings in acute and chronic deep vein thrombosis of the lower extremities. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics15050577","journal-title":"Diagnostics"},{"key":"3222_CR15","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1177\/17085381231153216","volume":"32","author":"S Zou","year":"2023","unstructured":"Zou S, Wu Z (2023) A narrative review of the application of machine learning in venous thromboembolism. Vascular 32:698\u2013704. https:\/\/doi.org\/10.1177\/17085381231153216","journal-title":"Vascular"},{"key":"3222_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2020.104221","volume":"141","author":"TD Martins","year":"2020","unstructured":"Martins TD, Annichino-Bizzacchi JM, Romano AVC, Maciel Filho R (2020) Artificial neural networks for prediction of recurrent venous thromboembolism. Int J Med Inform 141:104221. https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104221","journal-title":"Int J Med Inform"},{"key":"3222_CR17","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1186\/s13054-023-04683-4","volume":"27","author":"C Guan","year":"2023","unstructured":"Guan C, Ma F, Chang S, Zhang J (2023) Interpretable machine learning models for predicting venous thromboembolism in the intensive care unit: an analysis based on data from 207 centers. Crit Care 27:406. https:\/\/doi.org\/10.1186\/s13054-023-04683-4","journal-title":"Crit Care"},{"key":"3222_CR18","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1111\/ejh.14110","volume":"111","author":"T Chiasakul","year":"2023","unstructured":"Chiasakul T, Lam BD, McNichol M et al (2023) Artificial intelligence in the prediction of venous thromboembolism: a systematic review and pooled analysis. Eur J Haematol 111:951\u2013962. https:\/\/doi.org\/10.1111\/ejh.14110","journal-title":"Eur J Haematol"},{"key":"3222_CR19","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s12873-021-00447-x","volume":"21","author":"L He","year":"2021","unstructured":"He L, Luo L, Hou X et al (2021) Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model. BMC Emerg Med 21:60. https:\/\/doi.org\/10.1186\/s12873-021-00447-x","journal-title":"BMC Emerg Med"},{"key":"3222_CR20","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1186\/s12885-025-13630-1","volume":"25","author":"Y Zeng","year":"2025","unstructured":"Zeng Y, Chen Y, Zhu D et al (2025) Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer. BMC Cancer 25:220. https:\/\/doi.org\/10.1186\/s12885-025-13630-1","journal-title":"BMC Cancer"},{"key":"3222_CR21","doi-asserted-by":"publisher","first-page":"57.e1","DOI":"10.1016\/j.jtct.2022.10.007","volume":"29","author":"R-X Deng","year":"2023","unstructured":"Deng R-X, Zhu X-L, Zhang A-B et al (2023) Machine learning algorithm as a prognostic tool for venous thromboembolism in allogeneic transplant patients. Transplant Cell Ther 29:57.e1-57.e10. https:\/\/doi.org\/10.1016\/j.jtct.2022.10.007","journal-title":"Transplant Cell Ther"},{"key":"3222_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2022.108830","volume":"349","author":"A Bouchnita","year":"2022","unstructured":"Bouchnita A, Nony P, Llored J-P, Volpert V (2022) Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow. Math Biosci 349:108830. https:\/\/doi.org\/10.1016\/j.mbs.2022.108830","journal-title":"Math Biosci"},{"key":"3222_CR23","doi-asserted-by":"publisher","first-page":"4387","DOI":"10.1007\/s00521-023-09312-3","volume":"36","author":"TD Martins","year":"2024","unstructured":"Martins TD, Martins SD, Montalv\u00e3o S et al (2024) Combining artificial neural networks and hematological data to diagnose Covid-19 infection in Brazilian population. Neural Comput Appl 36:4387\u20134399. https:\/\/doi.org\/10.1007\/s00521-023-09312-3","journal-title":"Neural Comput Appl"},{"key":"3222_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-09915-4","author":"TD Martins","year":"2024","unstructured":"Martins TD, Maciel-Filho R, Montalv\u00e3o SAL et al (2024) Predicting mortality of cancer patients using artificial intelligence, patient data and blood tests. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-024-09915-4","journal-title":"Neural Comput Appl"},{"key":"3222_CR25","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1093\/imammb\/dqq005","volume":"28","author":"K Leiderman","year":"2011","unstructured":"Leiderman K, Fogelson AL (2011) Grow with the flow: a spatial-temporal model of platelet deposition and blood coagulation under flow. Math Med Biol 28:47\u201384. https:\/\/doi.org\/10.1093\/imammb\/dqq005","journal-title":"Math Med Biol"},{"key":"3222_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107259","volume":"102","author":"H Khajavi","year":"2025","unstructured":"Khajavi H, Hejazi K (2025) A predictive model of shear thrombus growth for the coronary arteries using integrated numerical simulation and artificial intelligence. Biomed Signal Process Control 102:107259. https:\/\/doi.org\/10.1016\/j.bspc.2024.107259","journal-title":"Biomed Signal Process Control"},{"key":"3222_CR27","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2023.1256462","author":"KN Shankar","year":"2023","unstructured":"Shankar KN, Diamond SL, Sinno T (2023) Development of a parallel multiscale 3D model for thrombus growth under flow. Front Phys. https:\/\/doi.org\/10.3389\/fphy.2023.1256462","journal-title":"Front Phys"},{"key":"3222_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/fluids7060197","author":"A Taebi","year":"2022","unstructured":"Taebi A (2022) Deep learning for computational hemodynamics: a brief review of recent advances. Fluids. https:\/\/doi.org\/10.3390\/fluids7060197","journal-title":"Fluids"},{"key":"3222_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2022.107676","volume":"98","author":"M Nabaei","year":"2022","unstructured":"Nabaei M (2022) Cerebral aneurysm evolution modeling from microstructural computational models to machine learning: a review. Comput Biol Chem 98:107676. https:\/\/doi.org\/10.1016\/j.compbiolchem.2022.107676","journal-title":"Comput Biol Chem"},{"key":"3222_CR30","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s12551-022-01040-7","volume":"15","author":"H Moradi","year":"2023","unstructured":"Moradi H, Al-Hourani A, Concilia G et al (2023) Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning. Biophys Rev 15:19\u201333. https:\/\/doi.org\/10.1007\/s12551-022-01040-7","journal-title":"Biophys Rev"},{"key":"3222_CR31","doi-asserted-by":"publisher","first-page":"7707","DOI":"10.3934\/mbe.2024339","volume":"21","author":"M Al Bannoud","year":"2024","unstructured":"Al Bannoud M, Martins TD, de Lima Montalv\u00e3o SA et al (2024) Integrating biomarkers for hemostatic disorders into computational models of blood clot formation: a systematic review. Math Biosci Eng 21:7707\u20137739. https:\/\/doi.org\/10.3934\/mbe.2024339","journal-title":"Math Biosci Eng"},{"key":"3222_CR32","doi-asserted-by":"publisher","first-page":"e1000097","DOI":"10.1371\/journal.pmed.1000097","volume":"6","author":"D Moher","year":"2009","unstructured":"Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6:e1000097. https:\/\/doi.org\/10.1371\/journal.pmed.1000097","journal-title":"PLoS Med"},{"key":"3222_CR33","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https:\/\/doi.org\/10.1136\/bmj.n71","journal-title":"BMJ"},{"key":"3222_CR34","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1182\/blood-2011-10-388140","volume":"120","author":"MH Flamm","year":"2012","unstructured":"Flamm MH, Colace TV, Chatterjee MS et al (2012) Multiscale prediction of patient-specific platelet function under flow. Blood 120:190\u2013198. https:\/\/doi.org\/10.1182\/blood-2011-10-388140","journal-title":"Blood"},{"key":"3222_CR35","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1093\/imammb\/dqw015","volume":"34","author":"Y Lu","year":"2017","unstructured":"Lu Y, Lee MY, Zhu S et al (2017) Multiscale simulation of thrombus growth and vessel occlusion triggered by collagen\/tissue factor using a data-driven model of combinatorial platelet signalling. Math Med Biol 34:523\u2013546. https:\/\/doi.org\/10.1093\/imammb\/dqw015","journal-title":"Math Med Biol"},{"key":"3222_CR36","doi-asserted-by":"publisher","first-page":"e1009850","DOI":"10.1371\/journal.pcbi.1009850","volume":"18","author":"KN Shankar","year":"2022","unstructured":"Shankar KN, Zhang Y, Sinno T, Diamond SL (2022) A three-dimensional multiscale model for the prediction of thrombus growth under flow with single-platelet resolution. PLoS Comput Biol 18:e1009850","journal-title":"PLoS Comput Biol"},{"key":"3222_CR37","doi-asserted-by":"publisher","DOI":"10.3390\/math11020289","author":"A Bouchnita","year":"2023","unstructured":"Bouchnita A, Mozokhina A, Nony P et al (2023) Combining computational modelling and machine learning to identify COVID-19 patients with a high thromboembolism risk. Mathematics. https:\/\/doi.org\/10.3390\/math11020289","journal-title":"Mathematics"},{"key":"3222_CR38","doi-asserted-by":"publisher","DOI":"10.3390\/axioms12090873","author":"A Bouchnita","year":"2023","unstructured":"Bouchnita A, Yadav K, Llored J-P et al (2023) Thrombin generation thresholds for coagulation initiation under flow. Axioms. https:\/\/doi.org\/10.3390\/axioms12090873","journal-title":"Axioms"},{"key":"3222_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106732","volume":"180","author":"Y Qian","year":"2024","unstructured":"Qian Y, Zhu G, Zhang Z et al (2024) Coagulo-Net: enhancing the mathematical modeling of blood coagulation using physics-informed neural networks. Neural Networks 180:106732. https:\/\/doi.org\/10.1016\/j.neunet.2024.106732","journal-title":"Neural Networks"},{"key":"3222_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-025-11070-3","author":"M Al Bannoud","year":"2025","unstructured":"Al Bannoud M, Martins TD, de Lima Montalv\u00e3o SA et al (2025) Prediction of recurrent venous thromboembolism using a spatiotemporal phenomenological model and artificial neural network. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-025-11070-3","journal-title":"Neural Comput Appl"},{"key":"3222_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-025-03837-5","author":"M Al Bannoud","year":"2025","unstructured":"Al Bannoud M, Martins TD, de Lima Montalv\u00e3o SA et al (2025) Determination of patient-specific blood coagulation kinetic parameters via neural networks: toward thrombosis prediction in personalized medicine. Ann Biomed Eng. https:\/\/doi.org\/10.1007\/s10439-025-03837-5","journal-title":"Ann Biomed Eng"},{"key":"3222_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.arcontrol.2024.100973","volume":"58","author":"MAl Bannoud","year":"2024","unstructured":"Bannoud M Al, da Silva CAM, Martins TD (2024) Applications of metaheuristic optimization algorithms in model predictive control for chemical engineering processes: a systematic review. Annu Rev Control 58:100973. https:\/\/doi.org\/10.1016\/j.arcontrol.2024.100973","journal-title":"Annu Rev Control"},{"key":"3222_CR43","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41540-021-00202-9","volume":"7","author":"DE Ghetmiri","year":"2021","unstructured":"Ghetmiri DE, Cohen MJ, Menezes AA (2021) Personalized modulation of coagulation factors using a thrombin dynamics model to treat trauma-induced coagulopathy. NPJ Syst Biol Appl 7:44. https:\/\/doi.org\/10.1038\/s41540-021-00202-9","journal-title":"NPJ Syst Biol Appl"},{"key":"3222_CR44","doi-asserted-by":"publisher","first-page":"1702","DOI":"10.1109\/TAC.2020.3000182","volume":"66","author":"J Berberich","year":"2020","unstructured":"Berberich J, K\u00f6hler J, M\u00fcller MA, Allg\u00f6wer F (2020) Data-driven model predictive control with stability and robustness guarantees. IEEE Trans Automat Contr 66:1702\u20131717","journal-title":"IEEE Trans Automat Contr"},{"key":"3222_CR45","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1080\/00986445.2024.2417901","volume":"212","author":"M Bannoud","year":"2025","unstructured":"Bannoud M, Al, Ferreira PHN, de Andrade RR, da Silva CAM (2025) Control of an integrated first and second-generation continuous alcoholic fermentation process with cell recycling using model predictive control. Chem Eng Commun 212:521\u2013544. https:\/\/doi.org\/10.1080\/00986445.2024.2417901","journal-title":"Chem Eng Commun"},{"key":"3222_CR46","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1038\/s41467-023-44231-w","volume":"15","author":"DE Ghetmiri","year":"2024","unstructured":"Ghetmiri DE, Venturi AJ, Cohen MJ, Menezes AA (2024) Quick model-based viscoelastic clot strength predictions from blood protein concentrations for cybermedical coagulation control. Nat Commun 15:314. https:\/\/doi.org\/10.1038\/s41467-023-44231-w","journal-title":"Nat Commun"},{"key":"3222_CR47","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.3595","volume":"38","author":"R M\u00e9ndez Rojano","year":"2022","unstructured":"M\u00e9ndez Rojano R, Zhussupbekov M, Antaki JF, Lucor D (2022) Uncertainty quantification of a thrombosis model considering the clotting assay PFA-100\u00ae. Int J Numer Methods Biomed Eng 38:e3595. https:\/\/doi.org\/10.1002\/cnm.3595","journal-title":"Int J Numer Methods Biomed Eng"},{"key":"3222_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmbbm.2021.104644","volume":"122","author":"L Antonini","year":"2021","unstructured":"Antonini L, Mandelli L, Berti F et al (2021) Validation of the computational model of a coronary stent: a fundamental step towards in silico trials. J Mech Behav Biomed Mater 122:104644","journal-title":"J Mech Behav Biomed Mater"},{"key":"3222_CR49","doi-asserted-by":"publisher","DOI":"10.3390\/ma15093302","volume":"15","author":"M Toma","year":"2022","unstructured":"Toma M, Singh-Gryzbon S, Frankini E et al (2022) Clinical impact of computational heart valve models. Materials 15:3302","journal-title":"Materials"},{"key":"3222_CR50","doi-asserted-by":"publisher","first-page":"1556","DOI":"10.1016\/j.jtcvs.2020.04.164","volume":"162","author":"P Kasinpila","year":"2021","unstructured":"Kasinpila P, Kong S, Fong R et al (2021) Use of patient-specific computational models for optimization of aortic insufficiency after implantation of left ventricular assist device. J Thorac Cardiovasc Surg 162:1556\u20131563","journal-title":"J Thorac Cardiovasc Surg"},{"key":"3222_CR51","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1177\/0267659120944105","volume":"36","author":"RG Conway","year":"2021","unstructured":"Conway RG, Zhang J, Jeudy J et al (2021) Computed tomography angiography as an adjunct to computational fluid dynamics for prediction of oxygenator thrombus formation. Perfusion 36:285\u2013292","journal-title":"Perfusion"}],"container-title":["Journal of Thrombosis and Thrombolysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11239-025-03222-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11239-025-03222-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11239-025-03222-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T13:41:57Z","timestamp":1780926117000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11239-025-03222-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":51,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["3222"],"URL":"https:\/\/doi.org\/10.1007\/s11239-025-03222-y","relation":{},"ISSN":["1573-742X"],"issn-type":[{"value":"1573-742X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,17]]},"assertion":[{"value":"20 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}