{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:58:48Z","timestamp":1790787528652,"version":"4.1.0"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Accurate prediction of bleeding events in patients receiving oral anticoagulants remains a key challenge in the management of atrial fibrillation (AF) and venous thromboembolism (VTE). Machine learning (ML) algorithms have emerged as powerful tools that capture complex, nonlinear interactions among risk factors, potentially offering superior accuracy. Objectives: To synthesize evidence comparing ML-based bleeding risk models with conventional clinical scores in anticoagulated AF and VTE populations. Methods: We conducted a systematic review with narrative synthesis of studies published between 2015 and 2025 applying ML algorithms to predict bleeding events in anticoagulated AF or VTE patients. Results: Thirteen studies were identified (seven AF and six VTE), including 464,523 participants in total. ML algorithms such as random forest (RF), extreme gradient boosting (XGBoost), and neural networks consistently outperformed traditional tools. In AF, AUCs ranged from 0.64 to 0.76 compared to 0.52\u20130.61 for HAS-BLED. In VTE, ML models achieved 0.59\u20130.91 versus 0.61\u20130.65 for RIETE or VTE-BLEED. Deep learning ensembles reached the highest AUCs (&gt;0.8). Conclusions: ML-based bleeding risk models demonstrated statistically superior discrimination compared to established scores in both AF and VTE contexts, but effect sizes were modest (\u0394AUC 0.05\u20130.15) and clinical utility remains uncertain. Broader validation, calibration assessment, and demonstration of impact on clinical outcomes are necessary before routine adoption.<\/jats:p>","DOI":"10.3390\/jcm15062370","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:44:41Z","timestamp":1773999881000},"page":"2370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Models for Predicting Bleeding Risk in Anticoagulated Patients with Atrial Fibrillation and Venous Thromboembolism: A Comparative Evidence Synthesis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4356-6146","authenticated-orcid":false,"given":"Winnie Z. Y.","family":"Teo","sequence":"first","affiliation":[{"name":"Department of Haematology-Oncology, National University Cancer Institute, Singapore 119074, Singapore"},{"name":"Fast and Chronic Programmes, Alexandra Hospital, Singapore 159964, Singapore"},{"name":"Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore"},{"name":"Department of Laboratory Medicine, Alexandra Hospital, Singapore 159964, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maggie Wing Yin","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Haematology-Oncology, National University Cancer Institute, Singapore 119074, Singapore"},{"name":"Fast and Chronic Programmes, Alexandra Hospital, Singapore 159964, Singapore"},{"name":"Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore"},{"name":"Department of Laboratory Medicine, Alexandra Hospital, Singapore 159964, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9598-0780","authenticated-orcid":false,"given":"Fang Jin","family":"Lim","sequence":"additional","affiliation":[{"name":"Fast and Chronic Programmes, Alexandra Hospital, Singapore 159964, Singapore"},{"name":"Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmeliene Su-Min","family":"Ong","sequence":"additional","affiliation":[{"name":"Fast and Chronic Programmes, Alexandra Hospital, Singapore 159964, Singapore"},{"name":"Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nesaretnam","family":"Barr Kumarakulasinghe","sequence":"additional","affiliation":[{"name":"Department of Haematology-Oncology, National University Cancer Institute, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7910-3154","authenticated-orcid":false,"given":"Eng Soo","family":"Yap","sequence":"additional","affiliation":[{"name":"Department of Haematology-Oncology, National University Cancer Institute, Singapore 119074, Singapore"},{"name":"Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore"},{"name":"Department of Laboratory Medicine, Ng Teng Fong General Hospital, Singapore 609606, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/S0140-6736(13)62343-0","article-title":"Comparison of the efficacy and safety of new oral anticoagulants with warfarin in patients with atrial fibrillation: A meta-analysis of randomised trials","volume":"383","author":"Ruff","year":"2014","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.chest.2015.11.026","article-title":"Antithrombotic therapy for VTE disease: CHEST guideline and expert panel report","volume":"149","author":"Kearon","year":"2016","journal-title":"Chest"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/S0140-6736(20)32658-1","article-title":"Venous thromboembolism","volume":"398","author":"Khan","year":"2021","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1378\/chest.10-0134","article-title":"A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: The Euro Heart Survey","volume":"138","author":"Pisters","year":"2010","journal-title":"Chest"},{"key":"ref_5","first-page":"3258","article-title":"The ORBIT bleeding score: Simple bedside score assess bleeding risk atrial fibrillation","volume":"36","author":"Simon","year":"2015","journal-title":"Eur. Heart J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.jacc.2011.03.031","article-title":"A new risk scheme to predict warfarin-associated hemorrhage: The ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) Study","volume":"58","author":"Fang","year":"2011","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/j.ahj.2005.04.017","article-title":"Clinical classification schemes for predicting hemorrhage: Results from the National Registry of Atrial Fibrillation (NRAF)","volume":"151","author":"Gage","year":"2006","journal-title":"Am. Heart J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.jacc.2012.06.019","article-title":"Performance of the HEMORR(2)HAGES, ATRIA, and HAS-BLED bleeding risk-prediction scores in patients with atrial fibrillation undergoing anticoagulation: The AMADEUS (evaluating the use of SR34006 compared to warfarin or acenocoumarol in patients with atrial fibrillation) study","volume":"60","author":"Apostolakis","year":"2012","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_9","first-page":"431","article-title":"A comparative analysis of HAS-BLED, ORBIT, DOAC, and AF-BLEED bleeding-risk scores in anticoagulated patients with atrial fibrillation: A report from the prospective Murcia atrial fibrillation project III (MAFP-III) cohort","volume":"24","author":"Lip","year":"2025","journal-title":"J. Thromb. Haemost."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.amjmed.2015.10.001","article-title":"Evaluation of the HAS-BLED, ATRIA, and ORBIT bleeding risk scores in patients with Atrial Fibrillation taking warfarin","volume":"129","author":"Senoo","year":"2016","journal-title":"Am. J. Med."},{"key":"ref_11","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":"ref_12","doi-asserted-by":"crossref","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":"ref_13","doi-asserted-by":"crossref","unstructured":"Kasim, S., Amir Rudin, P.N.F., Malek, S., Ibrahim, N., Kiew, X.N., Nasir, N.M., Ibrahim, K.S., and Raja Shariff, R.E. (2025). Enhanced cardiovascular risk prediction in the Western Pacific: A machine learning approach tailored to the Malaysian population. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0323949"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Weng, S.F., Reps, J., Kai, J., Garibaldi, J.M., and Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174944"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e70017","DOI":"10.1111\/eci.70017","article-title":"Machine learning in cardiovascular risk assessment: Towards a precision medicine approach","volume":"55","author":"Wang","year":"2025","journal-title":"Eur. J. Clin. Investig."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/sim.4085","article-title":"Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers","volume":"30","author":"Pencina","year":"2011","journal-title":"Stat. Med."},{"key":"ref_17","first-page":"215","article-title":"Comparison among RF, LR existing clinical risk scores predicting outcomes AF: J-RHYTHM registry","volume":"78","author":"Watanabe","year":"2021","journal-title":"J. Cardiol."},{"key":"ref_18","first-page":"117","article-title":"Predicting multi-faceted risks using machine learning AF: GLORIA-AF","volume":"5","author":"Lu","year":"2024","journal-title":"Eur. Heart J. Digit. Health"},{"key":"ref_19","first-page":"1032","article-title":"Machine learning predicts bleeding risk AF patients DOACs","volume":"125","author":"Chaudhary","year":"2025","journal-title":"Thromb. Haemost."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"132088","DOI":"10.1016\/j.ijcard.2024.132088","article-title":"Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation","volume":"407","author":"Bernardini","year":"2024","journal-title":"Int. J. Cardiol."},{"key":"ref_21","first-page":"579","article-title":"ML approaches predicting GIB antithrombotic users","volume":"14","author":"Herrin","year":"2021","journal-title":"Circ. Cardiovasc. Qual. Outcomes"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1055\/a-1754-7551","article-title":"Bleeding risk assessment in end-stage kidney disease: Validation of existing risk scores and evaluation of a machine learning-based approach","volume":"122","author":"Nopp","year":"2022","journal-title":"Thromb. Haemost."},{"key":"ref_23","first-page":"1159","article-title":"Deep learning predict bleeding extended anticoagulation","volume":"8","author":"Fard","year":"2024","journal-title":"Blood Adv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1111\/bjh.18737","article-title":"Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: Possibilities and limitations","volume":"201","author":"Mora","year":"2023","journal-title":"Br. J. Haematol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1016\/j.jtha.2023.12.034","article-title":"Developing a machine learning model for bleeding prediction in patients with cancer-associated thrombosis receiving anticoagulation therapy","volume":"22","author":"Grdinic","year":"2024","journal-title":"J. Thromb. Haemost."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102403","DOI":"10.1016\/j.rpth.2024.102403","article-title":"Machine learning analysis of bleeding status in venous thromboembolism patients","volume":"8","author":"Fard","year":"2024","journal-title":"Res. Pract. Thromb. Haemost."},{"key":"ref_27","first-page":"118","article-title":"External validation PREDICT-AI bleeding model","volume":"234","author":"Martin","year":"2024","journal-title":"Thromb. Res."},{"key":"ref_28","first-page":"102","article-title":"Prediction model major bleeding using ML NLP","volume":"232","author":"Martin","year":"2024","journal-title":"Thromb. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ"},{"key":"ref_30","doi-asserted-by":"crossref","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":"ref_31","doi-asserted-by":"crossref","first-page":"106126","DOI":"10.1016\/j.compbiomed.2022.106126","article-title":"Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation","volume":"150","author":"Lu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the future\u2014Big data, machine learning, and clinical medicine","volume":"375","author":"Obermeyer","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1001\/jama.2017.12126","article-title":"Discrimination and calibration of clinical prediction models: Users\u2019 guides to the medical literature","volume":"318","author":"Alba","year":"2017","journal-title":"JAMA"},{"key":"ref_34","unstructured":"Lundberg, S., and Lee, S.-I. (2017). A unified approach to interpreting model predictions. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Varma, S., and Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinform., 7.","DOI":"10.1186\/1471-2105-7-91"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Barr Kumarakulasinghe, N., Blomberg, T., Liu, J., Saraiva Leao, A., and Papapetrou, P. (2020). Evaluating local interpretable model-agnostic explanations on clinical machine learning classification models. Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, 28\u201330 July 2020, IEEE.","DOI":"10.1109\/CBMS49503.2020.00009"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"ref_40","unstructured":"Food Drug Administration (2021). Proposed Regulatory Framework Modifications Artificial Intelligence\/Machine Learning-Based Software Medical Device (AI\/ML-SaMD), FDA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2018.17163","article-title":"Clinical decision support in the era of artificial intelligence","volume":"320","author":"Shortliffe","year":"2018","journal-title":"JAMA"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e855","DOI":"10.1097\/PCC.0000000000003752","article-title":"Use of the area under the precision-recall curve to evaluate prediction models of rare critical illness events","volume":"26","author":"Martin","year":"2025","journal-title":"Pediatr. Crit. Care Med."},{"key":"ref_44","doi-asserted-by":"crossref","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":"ref_45","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMAScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/6\/2370\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T10:10:39Z","timestamp":1774001439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/6\/2370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,20]]},"references-count":45,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["jcm15062370"],"URL":"https:\/\/doi.org\/10.3390\/jcm15062370","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,20]]}}}