{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T13:11:43Z","timestamp":1789391503924,"version":"build-2803163510"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean Agency for Technology and Standard under the Ministry of Trade, Industry and Energy","award":["20022362"],"award-info":[{"award-number":["20022362"]}]},{"name":"Korean Agency for Technology and Standard under the Ministry of Trade, Industry and Energy","award":["2410003714"],"award-info":[{"award-number":["2410003714"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Myocardial infarction is one of the most severe forms of ischemic heart disease, associated with high mortality and disability worldwide. The accurate and reliable prediction of adverse cardiovascular events is critical for developing effective treatment strategies and improving outcomes in cardiac rehabilitation. Traditional prognostic models, such as the GRACE and TIMI scores, often lack the flexibility to incorporate a wide range of contemporary clinical predictors. Therefore, machine learning methods, particularly deep neural networks, have recently emerged as promising alternatives capable of enhancing predictive accuracy and enabling more personalized care. This study presents a multitask deep learning model designed to simultaneously address two related tasks: multidimensional binary classification of myocardial infarction complications and multiclass classification of mortality causes. The model was trained on a dataset of 1700 patients, encompassing 111 clinical and demographic features. Experimental results demonstrate high predictive accuracy and the model\u2019s capacity to capture complex interactions among risk factors, suggesting its potential as a valuable tool for clinical decision support in cardiology. Comparative analysis confirms that the proposed multitask approach performs comparably to, or better than, conventional machine learning models. Future research will focus on refining the model and validating its generalizability in real-world clinical environments.<\/jats:p>","DOI":"10.3390\/bioengineering12050520","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T10:27:41Z","timestamp":1747218461000},"page":"520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-0137","authenticated-orcid":false,"given":"Fazliddin","family":"Makhmudov","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Normakhmad","family":"Ravshanov","sequence":"additional","affiliation":[{"name":"Digital Technologies and Artificial Intelligence Development Research Institute, 17A, Buz-2, Tashkent 100125, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7688-6926","authenticated-orcid":false,"given":"Dilshot","family":"Akhmedov","sequence":"additional","affiliation":[{"name":"Digital Technologies and Artificial Intelligence Development Research Institute, 17A, Buz-2, Tashkent 100125, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleg","family":"Pekos","sequence":"additional","affiliation":[{"name":"Digital Technologies and Artificial Intelligence Development Research Institute, 17A, Buz-2, Tashkent 100125, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7070-0393","authenticated-orcid":false,"given":"Dilmurod","family":"Turimov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-7599","authenticated-orcid":false,"given":"Young-Im","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref_1","unstructured":"(2024). World Health Statistics 2024: Monitoring health for the SDGs, Sustainable Development Goals, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bokenberger, K., Rahman, S., Wang, M., Vaez, M., Dorner, T.E., Helgesson, M., Ivert, T., and Mittendorfer-Rutz, E. (2019). Work disability patterns before and after incident acute myocardial infarction and subsequent risk of common mental disorders: A Swedish cohort study. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-52487-w"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bauer, D., and Tou\u0161ek, P. (2021). Risk Stratification of Patients with Acute Coronary Syndrome. J. Clin. Med., 10.","DOI":"10.3390\/jcm10194574"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gupta, A.K., Mustafiz, C., Mutahar, D., Zaka, A., Parvez, R., Mridha, N., Stretton, B., Kovoor, J.G., Bacchi, S., and Ramponi, F. (2025). Machine Learning vs Traditional Approaches to Predict All-Cause Mortality for Acute Coronary Syndrome: A Systematic Review and Meta-analysis. Can. J. Cardiol.","DOI":"10.1016\/j.cjca.2025.01.037"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s11936-020-00814-0","article-title":"Deep Learning for Cardiovascular Risk Stratification","volume":"22","author":"Schlesinger","year":"2020","journal-title":"Curr. Treat. Options Cardiovasc. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Salas-Nu\u00f1ez, L.F., Barrera-Ocampo, A., Caicedo, P.A., Cortes, N., Osorio, E.H., Villegas-Torres, M.F., and Gonz\u00e1lez Barrios, A.F. (2024). Machine Learning to Predict Enzyme\u2013Substrate Interactions in Elucidation of Synthesis Pathways: A Review. Metabolites, 14.","DOI":"10.3390\/metabo14030154"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.2337\/db20-0586","article-title":"Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals with Prediabetes and Type 2 Diabetes","volume":"69","author":"Huang","year":"2020","journal-title":"Diabetes"},{"key":"ref_8","first-page":"3649406","article-title":"A comprehensive investigation of the performances of different machine learning classifiers with SMOTE-ENN oversampling technique and hyperparameter optimization for imbalanced heart failure dataset","volume":"2022","author":"Faisal","year":"2022","journal-title":"Sci. Program."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fedai, H., Sariisik, G., Toprak, K., Ta\u015fcanov, M.B., Efe, M.M., Ar\u011fa, Y., Do\u011fano\u011fullar\u0131, S., Gez, S., and Demirba\u011f, R. (2024). A Machine Learning Model for the Prediction of No-Reflow Phenomenon in Acute Myocardial Infarction Using the CALLY Index. Diagnostics, 14.","DOI":"10.3390\/diagnostics14242813"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"210410","DOI":"10.1109\/ACCESS.2020.3040166","article-title":"Explainable prediction of acute myocardial infarction using machine learning and Shapley Values","volume":"8","author":"Ibrahim","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Choi, A., Kim, M.J., Sung, J.M., Kim, S., Lee, J., Hyun, H., Kim, H.C., Kim, J.H., and Chang, H.-J., on behalf of the Connected Network for EMS Comprehensive Technical Support Using Artificial Intelligence Investigators (2022). Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database. J. Cardiovasc. Dev. Dis., 9.","DOI":"10.2196\/preprints.27405"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"478","DOI":"10.3390\/biomedinformatics3020033","article-title":"Detection of Myocardial Infarction Using Hybrid Models of Convolutional Neural Network and Recurrent Neural Network","volume":"3","author":"Hasbullah","year":"2023","journal-title":"BioMedInformatics"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zheng, H., Sherazi, S.W.A., and Lee, J.Y. (2024). A cost-sensitive deep neural network-based prediction model for the mortality in acute myocardial infarction patients with hypertension on imbalanced data. Front. Cardiovasc. Med., 11.","DOI":"10.3389\/fcvm.2024.1276608"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lee, S.-J., Lee, S.-H., Choi, H.-I., Lee, J.-Y., Jeong, Y.-W., Kang, D.-R., and Sung, K.-C. (2022). Deep learning improves prediction of cardiovascular disease-related mortality and admission in patients with hypertension: Analysis of the Korean national health information database. J. Clin. Med., 11.","DOI":"10.3390\/jcm11226677"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-024-01410-3","article-title":"A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction","volume":"8","author":"Lin","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1093\/jamia\/ocae114","article-title":"Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system","volume":"31","author":"Kim","year":"2024","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_17","first-page":"6046184","article-title":"Multitask Interactive Attention Learning Model Based on Hand Images for Assisting Chinese Medicine in Predicting Myocardial Infarction","volume":"2021","author":"Wang","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"113692","DOI":"10.1109\/ACCESS.2021.3099795","article-title":"A stacking ensemble prediction model for the occurrences of major adverse cardiovascular events in patients with acute coronary syndrome on imbalanced data","volume":"9","author":"Zheng","year":"2021","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e008678","DOI":"10.1161\/JAHA.118.008678","article-title":"An algorithm based on deep learning for predicting in-hospital cardiac arrest","volume":"7","author":"Kwon","year":"2018","journal-title":"J. Am. Heart Assoc."},{"key":"ref_20","first-page":"500","article-title":"Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: A 5-year multicentre prospective registry analysis","volume":"38","author":"Motwani","year":"2017","journal-title":"Eur. Heart J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.ymeth.2021.12.009","article-title":"Prediction of coronary heart disease based on combined reinforcement multitask progressive time-series networks","volume":"198","author":"Li","year":"2022","journal-title":"Methods"},{"key":"ref_22","first-page":"184","article-title":"Predicting Myocardial Infarction Complications and Outcomes with Deep Learning","volume":"23","author":"Yavru","year":"2022","journal-title":"Eski\u015fehir Tech. Univ. J. Sci. Technol. A-Appl. Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100319","DOI":"10.1016\/j.health.2024.100319","article-title":"A predictive approach for myocardial infarction risk assessment using machine learning and big clinical data","volume":"5","author":"Boudali","year":"2024","journal-title":"Healthc. Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.jacc.2016.05.049","article-title":"Predicting in-hospital mortality in patients with acute myocardial infarction","volume":"68","author":"McNamara","year":"2018","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"76003","DOI":"10.1109\/ACCESS.2024.3401744","article-title":"A Novel Deep Learning Approach for Myocardial Infarction Detection and Multi-Label Classification","volume":"12","author":"Abbas","year":"2024","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kwon, J., Jeon, K.-H., Kim, H.M., Kim, M.J., Lim, S., Kim, K.-H., Song, P.S., Park, J., Choi, R.K., and Oh, B.-H. (2019). Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224502"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s00779-019-01248-7","article-title":"Deep learning\u2013based prediction model of occurrences of major adverse cardiac events during 1-year follow-up after hospital discharge in patients with AMI using knowledge mining","volume":"26","author":"Kim","year":"2022","journal-title":"Pers. Ubiquit Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lu, J., Bennamoun, M., Stewart, J., Eshraghian, J., Liu, Y., Chow, B., Sanfilippo, F.M., and Dwivedi, G. (2023). Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients. arXiv.","DOI":"10.2139\/ssrn.4594346"},{"key":"ref_29","first-page":"278","article-title":"Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms","volume":"18","author":"Ghafari","year":"2023","journal-title":"J. Tehran Heart Cent."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shickel, B., Loftus, T.J., Ruppert, M., Upchurch, G.R., Ozrazgat-Baslanti, T., Rashidi, P., and Bihorac, A. (2023). Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-27418-5"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mandair, D., Tiwari, P., Simon, S., Colborn, K.L., and Rosenberg, M.A. (2020). Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01268-x"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"de Carvalho, L.S.F., Alexim, G., Nogueira, A.C.C., Fernandez, M.D., Rezende, T.B., Avila, S., Reis, R.T.B., Soares, A.A.M., and Sposito, A.C. (2023). The framing of time-dependent machine learning models improves risk estimation among young individuals with acute coronary syndromes. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-27776-0"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1093\/eurheartj\/ehad838","article-title":"Artificial intelligence: Revolutionizing cardiology with large language models","volume":"45","author":"Boonstra","year":"2024","journal-title":"Eur. Heart J."},{"key":"ref_34","unstructured":"Golovenkin, S.E., Gorban, A., Mirkes, E., Shulman, V.A., Rossiev, D.A., Shesternya, P.A., Nikulina, S.Y., Orlova, Y.V., and Dorrer, M.G. (2020). Myocardial Infarction Complications Database: Dataset, University of Leicester."},{"key":"ref_35","unstructured":"Golovenkin, S.E., Gorban, A.N., Mirkes, E.M., Shulman, V.A., Rossiev, D.A., Shesternya, P.A., Nikulina, S.Y., Orlova, Y.V., and Dorrer, M.G. (2024, February 18). Complications of Myocardial Infarction: A Database for Testing Recognition and Prediction Systems. Available online: https:\/\/figshare.le.ac.uk\/ndownloader\/files\/22803572."},{"key":"ref_36","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101361","DOI":"10.1016\/j.imu.2023.101361","article-title":"Predicting complications of myocardial infarction within several hours of hospitalization using data mining techniques","volume":"42","author":"Newaz","year":"2023","journal-title":"Inform. Med. Unlocked"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Farah, C., Adla, Y.A., and Awad, M. (2022, January 14\u201316). Can machine learning predict mortality in myocardial infarction patients within several hours of hospitalization? A comparative analysis. Proceedings of the IEEE 21st Mediterranean Electrotechnical Conference, Palermo, Italy.","DOI":"10.1109\/MELECON53508.2022.9842984"},{"key":"ref_39","unstructured":"Reddy, L., and Thangam, S. (2022, January 27\u201329). Predicting relapse of the myocardial infarction in hospitalized patients. Proceedings of the 3rd International Conference for Emerging Technology, Belgaum, India."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1007\/978-981-16-6285-0_63","article-title":"Early prognosis of acute myocardial infarction using machine learning techniques","volume":"Volume 91","author":"Joshi","year":"2022","journal-title":"Lecture Notes on Data Engineering and Communications Technologies"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tukhtaev, A., Turimov, D., Kim, J., and Kim, W. (2025). Feature Selection and Machine Learning Approaches for Detecting Sarcopenia Through Predictive Modeling. Mathematics, 13.","DOI":"10.3390\/math13010098"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Turimov, D., and Kim, W. (2025). Enhancing Sarcopenia Prediction Through an Ensemble Learning Approach: Addressing Class Imbalance for Improved Clinical Diagnosis. Mathematics, 13.","DOI":"10.3390\/math13010026"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/5\/520\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:32:37Z","timestamp":1760031157000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/5\/520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["bioengineering12050520"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12050520","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,14]]}}}