{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:47:16Z","timestamp":1782521236101,"version":"3.54.5"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100023544","name":"National Taiwan University Hospital Hsin-Chu Branch","doi-asserted-by":"crossref","award":["110-HCH023 and 111-HCH057"],"award-info":[{"award-number":["110-HCH023 and 111-HCH057"]}],"id":[{"id":"10.13039\/501100023544","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100023544","name":"National Taiwan University Hospital Hsin-Chu Branch","doi-asserted-by":"crossref","award":["110-HCH023 and 111-HCH057"],"award-info":[{"award-number":["110-HCH023 and 111-HCH057"]}],"id":[{"id":"10.13039\/501100023544","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-026-02470-3","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:14:29Z","timestamp":1775578469000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Interpretable machine learning models for stroke risk prediction in patients with newly diagnosed atrial fibrillation"],"prefix":"10.1038","volume":"9","author":[{"given":"Jesse Chih-Wei","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-Min","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng-Yu","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Lwun","family":"Ho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Kang","family":"Tu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao-Lun","family":"Lai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"2470_CR1","doi-asserted-by":"publisher","first-page":"100786","DOI":"10.1016\/j.lanepe.2023.100786","volume":"37","author":"D Linz","year":"2024","unstructured":"Linz, D. et al. Atrial fibrillation: epidemiology, screening and digital health. Lancet Reg. Health Eur. 37, 100786 (2024).","journal-title":"Lancet Reg. Health Eur."},{"key":"2470_CR2","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1177\/1747493019897870","volume":"16","author":"G Lippi","year":"2021","unstructured":"Lippi, G., Sanchis-Gomar, F. & Cervellin, G. Global epidemiology of atrial fibrillation: an increasing epidemic and public health challenge. Int. J. Stroke 16, 217\u2013221 (2021).","journal-title":"Int. J. Stroke"},{"key":"2470_CR3","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.tcm.2021.12.001","volume":"33","author":"I Escudero-Martinez","year":"2023","unstructured":"Escudero-Martinez, I., Morales-Caba, L. & Segura, T. Atrial fibrillation and stroke: a review and new insights. Trends Cardiovasc. Med. 33, 23\u201329 (2023).","journal-title":"Trends Cardiovasc. Med."},{"key":"2470_CR4","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1161\/CIRCRESAHA.120.316340","volume":"127","author":"J Kornej","year":"2020","unstructured":"Kornej, J., Borschel, C. S., Benjamin, E. J. & Schnabel, R. B. Epidemiology of atrial fibrillation in the 21st century: novel methods and new insights. Circ. Res. 127, 4\u201320 (2020).","journal-title":"Circ. Res."},{"key":"2470_CR5","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1161\/STROKEAHA.119.025554","volume":"51","author":"M Alberts","year":"2020","unstructured":"Alberts, M. et al. Risks of stroke and mortality in atrial fibrillation patients treated with rivaroxaban and warfarin. Stroke 51, 549\u2013555 (2020).","journal-title":"Stroke"},{"key":"2470_CR6","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.ahj.2013.05.016","volume":"166","author":"DA Garcia","year":"2013","unstructured":"Garcia, D. A. et al. Apixaban versus warfarin in patients with atrial fibrillation according to prior warfarin use: results from the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation trial. Am. Heart J. 166, 549\u2013558 (2013).","journal-title":"Am. Heart J."},{"key":"2470_CR7","volume":"48","author":"Y Bai","year":"2017","unstructured":"Bai, Y., Shantsila, A. & Lip, G. Y. H. Response by Bai et al to letter regarding article, \u201crivaroxaban versus dabigatran or warfarin in real-world studies of stroke prevention in atrial fibrillation: systematic review and meta-analysis. Stroke 48, e149 (2017).","journal-title":"Stroke"},{"key":"2470_CR8","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.122.026410","volume":"11","author":"WY Ding","year":"2022","unstructured":"Ding, W. Y. et al. Incidence and risk factors for residual adverse events despite anticoagulation in atrial fibrillation: results from phase II\/III of the GLORIA-AF registry. J. Am. Heart Assoc. 11, e026410 (2022).","journal-title":"J. Am. Heart Assoc."},{"key":"2470_CR9","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1016\/j.clinthera.2017.05.358","volume":"39","author":"AR Almutairi","year":"2017","unstructured":"Almutairi, A. R. et al. Effectiveness and safety of non-vitamin k antagonist oral anticoagulants for atrial fibrillation and venous thromboembolism: a systematic review and meta-analyses. Clin. Ther. 39, 1456\u20131478.e1436 (2017).","journal-title":"Clin. Ther."},{"key":"2470_CR10","doi-asserted-by":"publisher","first-page":"100797","DOI":"10.1016\/j.lanepe.2023.100797","volume":"37","author":"T-F Chao","year":"2024","unstructured":"Chao, T.-F., Potpara, T. S. & Lip, G. Y. H. Atrial fibrillation: stroke prevention. Lancet Reg. Health Eur. 37, 100797 (2024).","journal-title":"Lancet Reg. Health Eur."},{"key":"2470_CR11","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.hrthm.2015.08.017","volume":"13","author":"TF Chao","year":"2016","unstructured":"Chao, T. F. et al. Comparisons of CHADS2 and CHA2DS2-VASc scores for stroke risk stratification in atrial fibrillation: which scoring system should be used for Asians? Heart Rhythm 13, 46\u201353 (2016).","journal-title":"Heart Rhythm"},{"key":"2470_CR12","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1161\/CIR.0000000000001193","volume":"149","author":"JA Joglar","year":"2024","unstructured":"Joglar, J. A. et al. 2023 ACC\/AHA\/ACCP\/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the american college of cardiology\/american heart association joint committee on clinical practice guidelines. Circulation 149, e1\u2013e156 (2024).","journal-title":"Circulation"},{"key":"2470_CR13","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1093\/eurjpc\/zwab018","volume":"29","author":"TJ Siddiqi","year":"2022","unstructured":"Siddiqi, T. J. et al. Utility of the CHA2DS2-VASc score for predicting ischaemic stroke in patients with or without atrial fibrillation: a systematic review and meta-analysis. Eur. J. Prev. Cardiol. 29, 625\u2013631 (2022).","journal-title":"Eur. J. Prev. Cardiol."},{"key":"2470_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanepe.2024.100967","volume":"43","author":"K Teppo","year":"2024","unstructured":"Teppo, K. et al. Comparing CHA2DS2-VA and CHA2DS2-VASc scores for stroke risk stratification in patients with atrial fibrillation: a temporal trends analysis from the retrospective Finnish AntiCoagulation in Atrial Fibrillation (FinACAF) cohort. Lancet Reg. Health Eur. 43, 100967 (2024).","journal-title":"Lancet Reg. Health Eur."},{"key":"2470_CR15","doi-asserted-by":"publisher","first-page":"2391","DOI":"10.3390\/diagnostics14212391","volume":"14","author":"B Goh","year":"2024","unstructured":"Goh, B. & Bhaskar, S. M. Evaluating machine learning models for stroke prognosis and prediction in atrial fibrillation patients: a comprehensive meta-analysis. Diagnostics 14, 2391 (2024).","journal-title":"Diagnostics"},{"key":"2470_CR16","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1186\/s12872-025-04847-w","volume":"25","author":"W Xiong","year":"2025","unstructured":"Xiong, W. et al. Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation. BMC Cardiovasc Disord. 25, 429 (2025).","journal-title":"BMC Cardiovasc Disord."},{"key":"2470_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2024.132088","volume":"407","author":"A Bernardini","year":"2024","unstructured":"Bernardini, A. et al. Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation. Int. J. Cardiol. 407, 132088 (2024).","journal-title":"Int. J. Cardiol."},{"key":"2470_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106126","volume":"150","author":"J Lu","year":"2022","unstructured":"Lu, J. et al. Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation. Comput. Biol. Med. 150, 106126 (2022).","journal-title":"Comput. Biol. Med."},{"key":"2470_CR19","doi-asserted-by":"publisher","first-page":"769","DOI":"10.4103\/1673-5374.382228","volume":"19","author":"M Daidone","year":"2024","unstructured":"Daidone, M., Ferrantelli, S. & Tuttolomondo, A. Machine learning applications in stroke medicine: advancements, challenges, and future prospectives. Neural Regen. Res. 19, 769\u2013773 (2024).","journal-title":"Neural Regen. Res."},{"key":"2470_CR20","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s12012-024-09843-8","volume":"24","author":"B Truong","year":"2024","unstructured":"Truong, B. et al. Development and validation of machine learning algorithms to predict 1-year ischemic stroke and bleeding events in patients with atrial fibrillation and cancer. Cardiovasc. Toxicol. 24, 365\u2013374 (2024).","journal-title":"Cardiovasc. Toxicol."},{"key":"2470_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-51193-0","volume":"15","author":"PB Nielsen","year":"2024","unstructured":"Nielsen, P. B., Brondum, R. F., Nohr, A. K., Overvad, T. F. & Lip, G. Y. H. Risk of stroke in male and female patients with atrial fibrillation in a nationwide cohort. Nat. Commun. 15, 6728 (2024).","journal-title":"Nat. Commun."},{"key":"2470_CR22","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1093\/eurheartj\/ehad508","volume":"45","author":"H Buhari","year":"2024","unstructured":"Buhari, H. et al. Stroke risk in women with atrial fibrillation. Eur. Heart J. 45, 104\u2013113 (2024).","journal-title":"Eur. Heart J."},{"key":"2470_CR23","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.1093\/eurheartj\/ehw054","volume":"37","author":"Z Hijazi","year":"2016","unstructured":"Hijazi, Z. et al. The ABC (age, biomarkers, clinical history) stroke risk score: a biomarker-based risk score for predicting stroke in atrial fibrillation. Eur. Heart J. 37, 1582\u20131590 (2016).","journal-title":"Eur. Heart J."},{"key":"2470_CR24","doi-asserted-by":"crossref","unstructured":"Rivera-Caravaca, J. M. et al. Long-term stroke risk prediction in patients with atrial fibrillation: comparison of the ABC-Stroke and CHA2DS2-VASc scores. J. Am. Heart Assoc. 6, e006490 (2017).","DOI":"10.1161\/JAHA.117.006490"},{"key":"2470_CR25","doi-asserted-by":"publisher","first-page":"80","DOI":"10.31083\/j.fbl2703080","volume":"27","author":"S Jung","year":"2022","unstructured":"Jung, S. et al. Predicting ischemic stroke in patients with atrial fibrillation using machine learning. Front. Biosci. 27, 80 (2022).","journal-title":"Front. Biosci."},{"key":"2470_CR26","doi-asserted-by":"publisher","first-page":"e28034","DOI":"10.1016\/j.heliyon.2024.e28034","volume":"10","author":"A Papadopoulou","year":"2024","unstructured":"Papadopoulou, A., Harding, D., Slabaugh, G., Marouli, E. & Deloukas, P. Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank. Heliyon 10, e28034 (2024).","journal-title":"Heliyon"},{"key":"2470_CR27","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.121.022547","volume":"10","author":"R Zhang","year":"2021","unstructured":"Zhang, R. et al. Hemoglobin concentration and clinical outcomes after acute ischemic stroke or transient ischemic attack. J. Am. Heart Assoc. 10, e022547 (2021).","journal-title":"J. Am. Heart Assoc."},{"key":"2470_CR28","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1046\/j.1523-1755.2003.00109.x","volume":"64","author":"JL Abramson","year":"2003","unstructured":"Abramson, J. L., Jurkovitz, C. T., Vaccarino, V., Weintraub, W. S. & McClellan, W. Chronic kidney disease, anemia, and incident stroke in a middle-aged, community-based population: the ARIC Study. Kidney Int 64, 610\u2013615 (2003).","journal-title":"Kidney Int"},{"key":"2470_CR29","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157\u20131182 (2003).","journal-title":"J. Mach. Learn. Res."},{"key":"2470_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacadv.2024.101300","volume":"3","author":"M Naghavi","year":"2024","unstructured":"Naghavi, M. et al. AI-Enabled CT cardiac chamber volumetry predicts atrial fibrillation and stroke comparable to MRI. JACC Adv. 3, 101300 (2024).","journal-title":"JACC Adv."},{"key":"2470_CR31","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1161\/CIRCULATIONAHA.121.057480","volume":"145","author":"S Khurshid","year":"2022","unstructured":"Khurshid, S. et al. ECG-Based deep learning and clinical risk factors to predict atrial fibrillation. Circulation 145, 122\u2013133 (2022).","journal-title":"Circulation"},{"key":"2470_CR32","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.patrec.2020.05.035","volume":"136","author":"C Wang","year":"2020","unstructured":"Wang, C., Deng, C. & Wang, S. Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost. Pattern Recognit. Lett. 136, 190\u2013197 (2020).","journal-title":"Pattern Recognit. Lett."},{"key":"2470_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101987","volume":"111","author":"G Vandewiele","year":"2021","unstructured":"Vandewiele, G. et al. Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling. Artif. Intell. Med. 111, 101987 (2021).","journal-title":"Artif. Intell. Med."},{"key":"2470_CR34","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1093\/jamia\/ocac093","volume":"29","author":"R Van den Goorbergh","year":"2022","unstructured":"Van den Goorbergh, R., van Smeden, M., Timmerman, D. & Van Calster, B. The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J. Am. Med. Inf. Assoc. 29, 1525\u20131534 (2022).","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"2470_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2022.101845","volume":"96","author":"Z Li","year":"2022","unstructured":"Li, Z. Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Comput. Environ. Urban Syst. 96, 101845 (2022).","journal-title":"Comput. Environ. Urban Syst."},{"key":"2470_CR36","first-page":"CD002309","volume":"9","author":"J Chong","year":"2017","unstructured":"Chong, J., Leung, B. & Poole, P. Phosphodiesterase 4 inhibitors for chronic obstructive pulmonary disease. Cochrane Database Syst. Rev. 9, CD002309 (2017).","journal-title":"Cochrane Database Syst. Rev."},{"key":"2470_CR37","doi-asserted-by":"publisher","first-page":"e0179687","DOI":"10.1371\/journal.pone.0179687","volume":"12","author":"MK Son","year":"2017","unstructured":"Son, M. K., Lim, N.-K., Kim, H. W. & Park, H.-Y. Risk of ischemic stroke after atrial fibrillation diagnosis: a national sample cohort. PloS one 12, e0179687 (2017).","journal-title":"PloS one"},{"key":"2470_CR38","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1161\/STROKEAHA.123.044448","volume":"55","author":"J Putaala","year":"2024","unstructured":"Putaala, J. et al. Ischemic stroke temporally associated with new-onset atrial fibrillation: a population-based registry-linkage study. Stroke 55, 122\u2013130 (2024).","journal-title":"Stroke"},{"key":"2470_CR39","doi-asserted-by":"crossref","unstructured":"Collins, G. S. et al. TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, q902 (2024).","DOI":"10.1136\/bmj.q902"},{"key":"2470_CR40","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1097\/01.mlr.0000182534.19832.83","volume":"43","author":"H Quan","year":"2005","unstructured":"Quan, H. et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 43, 1130\u20131139 (2005).","journal-title":"Med Care"},{"key":"2470_CR41","first-page":"329","volume":"26","author":"YB Wah","year":"2018","unstructured":"Wah, Y. B., Ibrahim, N., Hamid, H. A., Abdul-Rahman, S. & Fong, S. Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy. Pertanika J. Sci. Technol. 26, 329\u2013340 (2018).","journal-title":"Pertanika J. Sci. Technol."},{"key":"2470_CR42","doi-asserted-by":"publisher","first-page":"5451","DOI":"10.1002\/sim.9921","volume":"42","author":"FM Ojeda","year":"2023","unstructured":"Ojeda, F. M. et al. Calibrating machine learning approaches for probability estimation: A comprehensive comparison. Stat. Med. 42, 5451\u20135478 (2023).","journal-title":"Stat. Med."},{"key":"2470_CR43","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s41512-019-0064-7","volume":"3","author":"AJ Vickers","year":"2019","unstructured":"Vickers, A. J., van Calster, B. & Steyerberg, E. W. A simple, step-by-step guide to interpreting decision curve analysis. Diagn. Progn. Res. 3, 18 (2019).","journal-title":"Diagn. Progn. Res."},{"key":"2470_CR44","doi-asserted-by":"publisher","first-page":"i6","DOI":"10.1136\/bmj.i6","volume":"352","author":"AJ Vickers","year":"2016","unstructured":"Vickers, A. J., Van Calster, B. & Steyerberg, E. W. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 352, i6 (2016).","journal-title":"BMJ"},{"key":"2470_CR45","doi-asserted-by":"publisher","first-page":"122","DOI":"10.7326\/M13-1522","volume":"160","author":"MJ Leening","year":"2014","unstructured":"Leening, M. J., Vedder, M. M., Witteman, J. C., Pencina, M. J. & Steyerberg, E. W. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician\u2019s guide. Ann. Intern Med. 160, 122\u2013131 (2014).","journal-title":"Ann. Intern Med."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02470-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02470-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02470-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:03:32Z","timestamp":1775581412000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02470-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,7]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2470"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02470-3","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,7]]},"assertion":[{"value":"29 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"289"}}