{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:07:55Z","timestamp":1792800475394,"version":"4.5.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Young Scientists Fund from the National Natural Science Foundation of China","award":["62301333"],"award-info":[{"award-number":["62301333"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Research Foundation of Education Department of Guangdong Province","award":["2022ZDJS115"],"award-info":[{"award-number":["2022ZDJS115"]}]},{"name":"XJTLU Research Development Fund","award":["RDF-21-02-068"],"award-info":[{"award-number":["RDF-21-02-068"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-026-03439-8","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T15:48:02Z","timestamp":1773676082000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating phenotypic information of obstructive sleep apnea and deep representation of sleep-event sequences for cardiovascular risk prediction"],"prefix":"10.1186","volume":"26","author":[{"given":"Yali","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengbi","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Min","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"3439_CR1","doi-asserted-by":"crossref","unstructured":"Rong M, Li K. A multi-type features fusion neural network for blood pressure prediction based on photoplethysmography. Biomed Signal Process Control. Jul 2021;68:102772.","DOI":"10.1016\/j.bspc.2021.102772"},{"key":"3439_CR2","doi-asserted-by":"crossref","unstructured":"Kannel WB, McGee DL. Diabetes and Cardiovascular Disease: The Framingham Study, JAMA. 1979;vol. 241, no. 19, pp. 2035\u20132038.","DOI":"10.1001\/jama.1979.03290450033020"},{"issue":"11","key":"3439_CR3","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1016\/S0195-668X(03)00114-3","volume":"24","author":"RM Conroy","year":"2003","unstructured":"Conroy RM, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987\u20131003.","journal-title":"Eur Heart J"},{"key":"3439_CR4","doi-asserted-by":"crossref","unstructured":"Mehilli J et al. Sex-based analysis of outcome in patients with acute myocardial infarction treated predominantly with percutaneous coronary intervention, Jama-Journal Of The American Medical Association. JAN 9 2002;vol. 287, no. 2, pp. 210\u2013215.","DOI":"10.1001\/jama.287.2.210"},{"key":"3439_CR5","doi-asserted-by":"crossref","unstructured":"Mohd Faizal AS, Thevarajah TM, Khor SM, Chang SW. A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Comput Methods Programs Biomed. Aug 2021;207:106190.","DOI":"10.1016\/j.cmpb.2021.106190"},{"key":"3439_CR6","doi-asserted-by":"crossref","unstructured":"Than MP et al. Machine Learning to Predict the Likelihood of Acute Myocardial Infarction, Circulation, vol. 140, no. 11, pp. 899\u2013909, 2019.","DOI":"10.1161\/CIRCULATIONAHA.119.041980"},{"key":"3439_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2019.04.027","volume":"179","author":"P Piros","year":"2019","unstructured":"Piros P, et al. Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry. Knowl Based Syst. 2019;179:1\u20137. 2019\/09\/01\/.","journal-title":"Knowl Based Syst"},{"key":"3439_CR8","doi-asserted-by":"crossref","unstructured":"Steele AJ, Cakiroglu SA, Shah AD, Denaxas SC, Hemingway H, Luscombe NM. Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease, bioRxiv, p. 256008, 2018.","DOI":"10.1101\/256008"},{"key":"3439_CR9","doi-asserted-by":"crossref","unstructured":"Wallert J, Tomasoni M, Madison G, Held C. Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data, BMC Medical Informatics and Decision Making, Article vol. 17, no. 1, 2017, Art. no. 99.","DOI":"10.1186\/s12911-017-0500-y"},{"issue":"4","key":"3439_CR10","doi-asserted-by":"publisher","first-page":"e0174944","DOI":"10.1371\/journal.pone.0174944","volume":"12","author":"SF Weng","year":"2017","unstructured":"Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE. 2017;12(4):e0174944.","journal-title":"PLoS ONE"},{"key":"3439_CR11","unstructured":"Goff DC et al. 2013 ACC\/AHA Guideline on the Assessment of Cardiovascular Risk, Circulation. 2014;vol. 129, no. 25_suppl_2, pp. S49-S73, 2014\/06\/24."},{"key":"3439_CR12","doi-asserted-by":"crossref","unstructured":"Dami S, Yahaghizadeh M. Predicting cardiovascular events with deep learning approach in the context of the internet of things. Neural Comput Appl. Jul 2021;33(13):7979\u201396.","DOI":"10.1007\/s00521-020-05542-x"},{"key":"3439_CR13","doi-asserted-by":"crossref","unstructured":"Chen H, Lundberg SM, Erion G, Kim JH, Lee SI. Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. NPJ Digit Med. Dec 8 2021;4, 1, p. 167.","DOI":"10.1038\/s41746-021-00536-y"},{"key":"3439_CR14","doi-asserted-by":"crossref","unstructured":"Sbrollini A et al. Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: A deep\u2013learning approach. BioMedical Eng Online Article 18. 2019;15, Art. 15.","DOI":"10.1186\/s12938-019-0630-9"},{"key":"3439_CR15","doi-asserted-by":"crossref","unstructured":"Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Critical Care Article 23. 2019;1, Art. 64.","DOI":"10.1186\/s13054-019-2351-7"},{"issue":"2","key":"3439_CR16","doi-asserted-by":"publisher","first-page":"e0211057","DOI":"10.1371\/journal.pone.0211057","volume":"14","author":"DA Kaji","year":"2019","unstructured":"Kaji DA, et al. An attention based deep learning model of clinical events in the intensive care unit. PLoS ONE. 2019;14(2):e0211057.","journal-title":"PLoS ONE"},{"key":"3439_CR17","unstructured":"Barrett LA, Payrovnaziri SN, Bian J, He Z. Building computational models to predict one-year mortality in ICU patients with acute myocardial infarction and post myocardial infarction syndrome, AMIA Jt Summits Transl Sci Proc. 2019;Article vol. 2019, pp. 407\u2013416."},{"key":"3439_CR18","unstructured":"Javaheri S et al. Sleep Apnea: types, mechanisms, and clinical cardiovascular consequences. J Am Coll Cardiol. Feb 21 2017;69, 7, pp. 841\u201358."},{"key":"3439_CR19","doi-asserted-by":"crossref","unstructured":"Strassberger C, et al. Beyond the AHI-pulse wave analysis during sleep for recognition of cardiovascular risk in sleep apnea patients. J Sleep Res. Dec 2021;30(6):e13364.","DOI":"10.1111\/jsr.13364"},{"key":"3439_CR20","doi-asserted-by":"crossref","unstructured":"Kim JW, Won TB, Rhee CS, Park YM, Yoon IY, Cho SW. Polysomnographic phenotyping of obstructive sleep apnea and its implications in mortality in Korea, Sci Rep. Aug 6 2020;vol. 10, no. 1, p. 13207.","DOI":"10.1038\/s41598-020-70039-5"},{"key":"3439_CR21","doi-asserted-by":"crossref","unstructured":"Mazzotti DR, Keenan BT, Lim DC, Gottlieb DJ, Kim J, Pack AI. Symptom Subtypes of Obstructive Sleep Apnea Predict Incidence of Cardiovascular Outcomes. Am J Respir Crit Care Med. Aug 15 2019;200, 4, pp. 493\u2013506.","DOI":"10.1164\/rccm.201808-1509OC"},{"key":"3439_CR22","doi-asserted-by":"crossref","unstructured":"Zinchuk AV et al. Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea, Thorax. May 2018;vol. 73, no. 5, pp. 472\u2013480.","DOI":"10.1136\/thoraxjnl-2017-210431"},{"key":"3439_CR23","doi-asserted-by":"crossref","unstructured":"Lacedonia D et al. Characterization of obstructive sleep apnea-hypopnea syndrome (OSA) population by means of cluster analysis. J Sleep Res. Dec 2016;25, 6, pp. 724\u201330.","DOI":"10.1111\/jsr.12429"},{"key":"3439_CR24","doi-asserted-by":"crossref","unstructured":"Saki N et al. Impact of modifiable risk factors on prediction of 10-year cardiovascular disease utilizing framingham risk score in Southwest Iran. BMC Cardiovasc Disord. Jul 2023;23, 1, Art. 358.","DOI":"10.1186\/s12872-023-03388-4"},{"key":"3439_CR25","doi-asserted-by":"crossref","unstructured":"Peng M et al. A Cardiovascular Disease Risk Score Model Based on High Contribution Characteristics, Applied Sciences. 2023;13, 2, p. 893.","DOI":"10.3390\/app13020893"},{"key":"3439_CR26","doi-asserted-by":"crossref","unstructured":"Park S et al. May., Prediction of the 10-year risk of atherosclerotic cardiovascular disease in the Korean population. Epidemiol Health. 2023;45, Art. no. e2023052.","DOI":"10.4178\/epih.e2023052"},{"key":"3439_CR27","doi-asserted-by":"crossref","unstructured":"Hathaway QA, Yanamala N, Budoff MJ, Sengupta PP, Zeb I. Deep neural survival networks for cardiovascular risk prediction: The Multi-Ethnic Study of Atherosclerosis (MESA). Comput Biol Med. Dec 2021;139:104983.","DOI":"10.1016\/j.compbiomed.2021.104983"},{"key":"3439_CR28","doi-asserted-by":"crossref","unstructured":"Zhang GQ et al. The National Sleep Research Resource: towards a sleep data commons, (in eng), J Am Med Inform Assoc. Oct 1 2018;vol. 25, no. 10, pp. 1351\u20131358.","DOI":"10.1093\/jamia\/ocy064"},{"key":"3439_CR29","doi-asserted-by":"crossref","unstructured":"Chen X et al. Racial\/Ethnic Differences in Sleep Disturbances: The Multi-Ethnic Study of Atherosclerosis (MESA), (in eng), Sleep. Jun 1 2015;38, 6, pp. 877\u2009\u2013\u200988.","DOI":"10.5665\/sleep.4732"},{"key":"3439_CR30","unstructured":"Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30."},{"key":"3439_CR31","doi-asserted-by":"crossref","unstructured":"Shahrbabaki SS, Linz D, Hartmann S, Redline S, Baumert M. Sleep arousal burden is associated with long-term all-cause and cardiovascular mortality in 8001 community-dwelling older men and women. Eur Heart J. Jun 1 2021;42, 21, pp. 2088\u201399.","DOI":"10.1093\/eurheartj\/ehab151"},{"key":"3439_CR32","doi-asserted-by":"crossref","unstructured":"Azarbarzin A et al. The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study. Eur Heart J. Apr 7 2019;40, 14, pp. 1149\u201357.","DOI":"10.1093\/eurheartj\/ehy624"},{"key":"3439_CR33","doi-asserted-by":"crossref","unstructured":"Kendzerska T, Mollayeva T, Gershon AS, Leung RS, Hawker G, Tomlinson G. Untreated obstructive sleep apnea and the risk for serious long-term adverse outcomes: A systematic review, Sleep Medicine Reviews. 2014;vol. 18, no. 1, pp. 49\u201359, 2014\/02\/01\/.","DOI":"10.1016\/j.smrv.2013.01.003"},{"issue":"1","key":"3439_CR34","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s00424-011-1055-x","volume":"463","author":"RST Leung","year":"2011","unstructured":"Leung RST, Comondore VR, Ryan CM, Stevens D. Mechanisms of sleep-disordered breathing: causes and consequences. Pfl\u00fcgers Archiv - Eur J Physiol. 2011;463(1):213\u201330.","journal-title":"Pfl\u00fcgers Archiv - Eur J Physiol"},{"key":"3439_CR35","unstructured":"Yeghiazarians Y et al. Obstructive Sleep Apnea and Cardiovascular Disease: A Scientific Statement From the American Heart Association, Circulation. Jul 20 2021;vol. 144, no. 3, pp. e56-e67."},{"issue":"2","key":"3439_CR36","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s11760-017-1146-z","volume":"12","author":"LD Sharma","year":"2018","unstructured":"Sharma LD, Sunkaria RK. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image Video Process Article vol. 2018;12(2):199\u2013206.","journal-title":"Signal Image Video Process Article vol"},{"key":"3439_CR37","doi-asserted-by":"crossref","unstructured":"Lui HW, Chow KL. Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices, Informatics in Medicine Unlocked2018;vol. 13, pp. 26\u201333, 2018\/01\/01\/.","DOI":"10.1016\/j.imu.2018.08.002"},{"key":"3439_CR38","first-page":"337","volume":"869","author":"AM Lodhi","year":"2018","unstructured":"Lodhi AM, Qureshi AN, Sharif U, Ashiq Z. A novel approach using voting from ecg leads to detect myocardial infarction. Adv Intell Syst Comput. 2018;869:337\u201352.","journal-title":"Adv Intell Syst Comput"},{"key":"3439_CR39","doi-asserted-by":"crossref","unstructured":"Dohare AK, Kumar V, Kumar R. Detection of myocardial infarction in 12 lead ECG using support vector machine, Applied Soft Computing. 2018;vol. 64, pp. 138\u2013147, 2018\/03\/01\/.","DOI":"10.1016\/j.asoc.2017.12.001"},{"key":"3439_CR40","doi-asserted-by":"crossref","unstructured":"Shlomai G, Shemesh J, Segev S, Koren-Morag N, Grossman E. Jul, The Multi-Ethnic Study of Atherosclerosis-Calcium Score Improves Statin Treatment Allocation in Asymptomatic Adults. Front Cardiovasc Med, 9, 2022, Art. 855390.","DOI":"10.3389\/fcvm.2022.855390"},{"key":"3439_CR41","doi-asserted-by":"crossref","unstructured":"Mehta R, et al. Ten-Year Risk-Prediction Equations for Incident Heart Failure Hospitalizations in Chronic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort Study and the Multi- Ethnic Study of Atherosclerosis. J Card Fail. Apr 2022;28(4):540\u201350.","DOI":"10.1016\/j.cardfail.2021.10.007"},{"key":"3439_CR42","doi-asserted-by":"crossref","unstructured":"Rundo JV, Downey R 3. Polysomnography, (in eng). Handb Clin Neurol. 2019;160:381\u201392.","DOI":"10.1016\/B978-0-444-64032-1.00025-4"},{"key":"3439_CR43","doi-asserted-by":"crossref","unstructured":"Cuellar NG. The effects of periodic limb movements in sleep (PLMS) on cardiovascular disease. Heart Lung. Sep-Oct 2013;42, 5, pp. 353\u201360.","DOI":"10.1016\/j.hrtlng.2013.07.006"},{"key":"3439_CR44","doi-asserted-by":"crossref","unstructured":"Sabanayagam C, Shankar A. Aug, Sleep duration and cardiovascular disease: results from the National Health Interview Survey, (in eng), Sleep. 2010;33, 8, pp. 1037\u201342.","DOI":"10.1093\/sleep\/33.8.1037"},{"issue":"3","key":"3439_CR45","doi-asserted-by":"publisher","first-page":"94","DOI":"10.3390\/jcdd10030094","volume":"10","author":"M Koutentakis","year":"2023","unstructured":"Koutentakis M, Surma S, Rogula S, Filipiak KJ, G\u0105secka A. The Effect of a Vegan Diet on the Cardiovascular System. J Cardiovasc Dev Disease. 2023;10(3):94.","journal-title":"J Cardiovasc Dev Disease"},{"key":"3439_CR46","doi-asserted-by":"crossref","unstructured":"Dy\u0144ka D, Kowalcze K, Charuta A, Paziewska A. The Ketogenic Diet and Cardiovascular Diseases, Nutrients. 2023;15, 15:3368.","DOI":"10.3390\/nu15153368"},{"key":"3439_CR47","doi-asserted-by":"crossref","unstructured":"Yu E, Malik VS, Hu FB. Cardiovascular Disease Prevention by Diet Modification JACC Health Promotion Series. J Am Coll Cardiol. Aug 2018;72(8):914\u201326.","DOI":"10.1016\/j.jacc.2018.02.085"},{"key":"3439_CR48","doi-asserted-by":"crossref","unstructured":"Pan A, Lin X, Hemler E, Hu FB. Diet and Cardiovascular Disease: Advances and Challenges in Population-Based Studies, (in eng), Cell Metab. Mar 6 2018;vol. 27, no. 3, pp. 489\u2013496.","DOI":"10.1016\/j.cmet.2018.02.017"},{"key":"3439_CR49","doi-asserted-by":"crossref","unstructured":"Ding FH, et al. Polysomnographic validation of an under-mattress monitoring device in estimating sleep architecture and obstructive sleep apnea in adults. Sleep Med. Aug 2022;96:20\u20137.","DOI":"10.1016\/j.sleep.2022.04.010"},{"key":"3439_CR50","doi-asserted-by":"crossref","unstructured":"Hassan AR, Bhuiyan MIH. An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting, Neurocomputing. Jan 2017; vol. 219, pp. 76\u201387.","DOI":"10.1016\/j.neucom.2016.09.011"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03439-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03439-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03439-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T02:22:18Z","timestamp":1776910938000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03439-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,16]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3439"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03439-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4084889\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,16]]},"assertion":[{"value":"12 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","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"}},{"value":"To adhere to the TRIPOD guidelines, we have completed the TRIPOD checklist in Supplementary File S2, detailing how each guideline item has been addressed in our study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Supplementary information"}},{"value":"Data Consent and Privacy\n                      .","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"To ensure that the research objective of this study conforms to the original MESA study, a formal research protocol has been submitted to and approved by the provider of the MESA dataset. Ethical approval was obtained from the Ethics Review Board of Shenzhen Technology University (No: SZTUEA-20225011, date: 18 May 2022) to ensure that this study complies with the local ethical standards. With respect to data privacy, the authors have implemented rigorous access controls, restricting access to the MESA data to authorized researchers and staff only.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"137"}}