学术急诊医学档案

Johansson S, Rosengren A, Young K, Jennings EJBcd. Mortality and morbidity trends after the first year in survivors of acute myocardial infarction: a systematic review. 2017;17(1):53.

Sachdeva P, Kaur K, Fatima S, Mahak F, Noman M, Siddenthi SM, et al. Advancements in myocardial infarction management: exploring novel approaches and strategies. 2023;15(9).

Reed GW, Rossi JE, Cannon CPJTL. Acute myocardial infarction. 2017;389(10065):197-210.

Wenzl FA, Kraler S, Ambler G, Weston C, Herzog SA, Räber L, et al. Sex-specific evaluation and redevelopment of the GRACE score in non-ST-segment elevation acute coronary syndromes in populations from the UK and Switzerland: a multinational analysis with external cohort validation. 2022;400(10354):744-56.

Cho SM, Austin PC, Ross HJ, Abdel-Qadir H, Chicco D, Tomlinson G, et al. Machine learning compared with conventional statistical models for predicting myocardial infarction readmission and mortality: a systematic review. 2021;37(8):1207-14.

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. 2021;372.

Schiavo JHJMrsq. PROSPERO: an international register of systematic review protocols. 2019;38(2):171-80.

Lee J, Mulder F, Leeflang M, Wolff R, Whiting P, Bossuyt PMJAoim. QUAPAS: an adaptation of the QUADAS-2 tool to assess prognostic accuracy studies. 2022;175(7):1010-8.

Khatami R, Faghihi M, Khatami H, Yousefifard M, Khatami SJAoAEM. Calculation of Sensitivity and Specificity from Partial Data for Meta-Analyses: Introducing Some Practical Methods. 2025;13(1):e56-e.

Deeks JJ, Macaskill P, Irwig LJJoce. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. 2005;58(9):882-93.

Alonso-Coello P, Schünemann HJ, Moberg J, Brignardello-Petersen R, Akl EA, Davoli M, et al. GRADE Evidence to Decision (EtD) frameworks: a systematic and transparent approach to making well informed healthcare choices. 1: Introduction. 2016;353.

Molenaar MA, Bouma BJ, Asselbergs FW, Verouden NJ, Selder JL, Chamuleau SA, et al. Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome. 2024;5(2):170-82.

Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. 2017;38(7):500-7.

Pezel T, Sanguineti F, Garot P, Unterseeh T, Champagne S, Toupin S, et al. Machine-learning score using stress CMR for death prediction in patients with suspected or known CAD. 2022;15(11):1900-13.

Collins GS, Moons KG, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. 2024;385.

Moons KG, Damen JA, Kaul T, Hooft L, Navarro CA, Dhiman P, et al. PROBAST+ AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. 2025;388.

Alcober GMI, Lagman AC, Revano TF, editors. Predicting the Mortality of Female Patients suffering from Myocardial Infarction using Data Mining Methods: A Comparison. 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM); 2020: IEEE.

Aziz F, Malek S, Ibrahim KS, Raja Shariff RE, Wan Ahmad WA, Ali RM, et al. Short-and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach. 2021;16(8):e0254894.

Bai Z, Lu J, Li T, Ma Y, Liu Z, Zhao R, et al. Clinical Feature‐Based Machine Learning Model for 1‐Year Mortality Risk Prediction of ST‐Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study. 2021;2021(1):7252280.

Barrett LA, Payrovnaziri SN, Bian J, He ZJASoTSP. Building computational models to predict one-year mortality in ICU patients with acute myocardial infarction and post myocardial infarction syndrome. 2019;2019:407.

Chen P, Wang B, Zhao L, Ma S, Wang Y, Zhu Y, et al. Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus. 2023;23(1):585.

Chen Z, Zhang L, Li R, Wang J, Chen L, Jin Y, et al. The development and validation of a prognostic prediction modeling study in acute myocardial infarction patients after percutaneous coronary intervention: hemorrhea and major cardiovascular adverse events. 2024;16(9):6216.

Cheng C-Y, Chiu I-M, Lin C-HR, Lin X-H, Chen F-C, Hsu T-Y, editors. Stepwise regression machine learning models for in-hospital mortality prediction in patients after ST-segment slevation myocardial infarction (STEMI). 2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD); 2022: IEEE.

Cosentino N, Resta ML, Somaschini A, Campodonico J, D’Aleo G, Di Stefano G, et al. ST-Segment elevation acute myocardial infarction complicated by cardiogenic shock: early predictors of very long-term mortality. 2021;10(11):2237.

Deng L, Zhao X, Su X, Zhou M, Huang D, Zeng XJBMI, et al. Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention. 2022;22(1):109.

Farah C, Adla YA, Awad M, editors. Can machine learning predict mortality in myocardial infarction patients within several hours of hospitalization? A comparative analysis. 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON); 2022: IEEE.

Geltser, B. I., et al. "Comparative Analysis of the Effectiveness of Riskometer Scales in Predicting the Risk of in-Hospital Mortality in Patients With ST-Segment Elevation Myocardial Infarction After Percutaneous Coronary Intervention." Kardiologiia 64.8 (2024): 48-55.

Ghafari R, Azar AS, Ghafari A, Aghdam FM, Valizadeh M, Khalili N, et al. Prediction of the fatal acute complications of myocardial infarction via machine learning algorithms. 2023;18(4):278.

Gong M, Liang D, Xu D, Jin Y, Wang G, Shan PJCiB, et al. Analyzing predictors of in-hospital mortality in patients with acute ST-segment elevation myocardial infarction using an evolved machine learning approach. 2024;170:107950.

Gupta MD, Goyal D, Kunal S, Shetty MK, Girish M, Batra V, et al. Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients. 2025.

Hadanny A, Shouval R, Wu J, Shlomo N, Unger R, Zahger D, et al. Predicting 30-day mortality after ST elevation myocardial infarction: Machine learning-based random forest and its external validation using two independent nationwide datasets. 2021;78(5):439-46.

Halim MHA, Yusoff YS, Yusuf MMJIJE. Predicting sudden deaths following myocardial infarction in Malaysia using machine learning classifiers. 2018;7(415):4-6.

Helgestad OK, Povlsen AL, Josiassen J, Möller S, Hassager C, Jensen LO, et al. Data-driven point-of-care risk model in patients with acute myocardial infarction and cardiogenic shock. 2021;10(6):668-75.

Hsieh M-H, Lin S-Y, Lin C-L, Hsieh M-J, Hsu W-H, Ju S-W, et al. A fitting machine learning prediction model for short-term mortality following percutaneous catheterization intervention: a nationwide population-based study. 2019;7(23):732.

Iftikhar A, Bond RR, Rjoob K, Knoery C, Leslie SJ, McShane A, et al., editors. Machine learning to predict 30 days and 1-year mortality in STEMI and turndown patients. 2020 Computing in Cardiology; 2020: IEEE.

Islam SA, Islam MM, Kahraman HA, Ciril MF, Mizrak A, Tayfur IJA. Developing and validating a new model to predict in-hospital mortality in patients with acute myocardial infarction. 2023;59:12.

Kashirina I, Firyulina M, editors. Building models for predicting mortality after myocardial infarction in conditions of unbalanced classes, including the influence of weather conditions. CEUR Workshop Proceedings; 2020.

Kashirina IL, Firyulina MA, Bondarenko YV, Desyatirikova EN, Efimova OE, Chernenkaya LV, editors. Identification of risk factors for mortality after myocardial infarction using machine learning methods. 2021 XXIV International Conference on Soft Computing and Measurements (SCM); 2021: IEEE.

Kasim S, Malek S, Cheen S, Safiruz MS, Ahmad WAW, Ibrahim KS, et al. In-hospital risk stratification algorithm of Asian elderly patients. 2022;12(1):17592.

Kasim S, Amir Rudin PNF, Malek S, Ibrahim KS, Wan Ahmad WA, Fong AYY, et al. Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI). 2024;14(1):12378.

Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, et al. Use of machine learning models to predict death after acute myocardial infarction. 2021;6(6):633-41.

Kononova Y, Abramyan L, Derevitskii I, Babenko AJJoPM. Predictors of Carbohydrate Metabolism Disorders and Lethal Outcome in Patients after Myocardial Infarction: A Place of Glucose Level. 2023;13(6):997.

Kwon J-m, Jeon K-H, Kim HM, Kim MJ, Lim S, Kim K-H, et al. Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction. 2019;14(10):e0224502.

Lee W, Lee J, Woo S-I, Choi SH, Bae J-W, Jung S, et al. Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction. 2021;11(1):12886.

Lee J, Lee S, Street WN, Polgreen LAJBMI, Making D. Machine learning approaches to predict the 1-year-after-initial-AMI survival of elderly patients. 2022;22(1):115.

Li Y-m, Jiang L-c, He J-j, Jia K-y, Peng Y, Chen MJT, et al. Machine learning to predict the 1-year mortality rate after acute anterior myocardial infarction in Chinese patients. 2020:1-6.

Li Y-W, Yang X-D, Cai Y-C, Kong X-Y, editors. Artificial intelligence-based prediction of acute myocardial infarction mortality risk. 2023 2nd International Conference on Health Big Data and Intelligent Healthcare (ICHIH); 2023: IEEE.

Li P, Yao W, Wu J, Gao Y, Zhang X, Hu WJC, et al. Development and validation of a nomogram model for predicting in-hospital mortality in non-diabetic patients with non-ST-segment elevation acute myocardial infarction. 2024;30:10760296241276524.

Lin X, Pan X, Yang Y, Yang W, Wang X, Zou K, et al. Machine learning models to predict 30-day mortality for critical patients with myocardial infarction: a retrospective analysis from MIMIC-IV database. 2024;11:1368022.

Mohammad MA, Olesen KK, Koul S, Gale CP, Rylance R, Jernberg T, et al. Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital for heart failure after myocardial infarction: a nationwide population-based study. 2022;4(1):e37-e45.

Niedziela JT, Cieśla D, Wojakowski W, Gierlotka M, Dudek D, Witkowski A, et al. Is neural network better than logistic regression in death prediction in patients after ST-segment elevation myocardial infarction? 2021;79(12):1353-61.

Oliveira M, Seringa J, Pinto FJ, Henriques R, Magalhães TJBMI, Making D. Machine learning prediction of mortality in acute myocardial infarction. 2023;23(1):70.

Peng P-C, Chien H-C, Mitra P, Pai T-W, Wang C-H, Liu M-H, editors. Using machine learning approach to predict short-term mortality risk of acute myocardial infarction after emergency admission. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2021: IEEE.

Piros P, Ferenci T, Fleiner R, Andréka P, Fujita H, Főző L, et al. Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry. 2019;179:1-7.

Piros P, Fleiner R, Kovács L, editors. Random Forest-based predictive modelling on Hungarian Myocardial Infarction Registry. 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE); 2020: IEEE.

Razavi SR, Szun T, Zaremba AC, Shah AH, Moussavi ZJM. 1-Year mortality prediction through artificial intelligence using hemodynamic trace analysis among patients with ST elevation myocardial infarction. 2024;60(4):558.

Razavi SR, Zaremba AC, Szun T, Cheung S, Shah AH, Moussavi ZJCiB, et al. Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms. 2025;184:109439.

Romanov N, Popa IV, Burlacu A, Brinza C, Fotache M, editors. A Comparison of Interpretable Machine Learning Models to Predict In-Hospital Mortality After Myocardial Infarction: Analyzing Two Years Data from a High-Volume Interventional Center. Proceedings of Seventh International Congress on Information and Communication Technology: ICICT 2022, London, Volume 3; 2022: Springer.

Roudini B, Khajehpiri B, Moghaddam HA, Forouzanfar MJIM. Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data. 2024;4(03):170-6.

Salman IJTJoEE, Sciences C. Heart attack mortality prediction: an application of machine learning methods. 2019;27(6):4378-89.

Shakhgeldyan K, Kuksin N, Domzhalov I, Geltser BJСтвм. Performance of the Models Predicting In-Hospital Mortality in Patients with ST-Segment Elevation Myocardial Infarction with Predictors in Categorical and Continuous Forms. 2024;16(1 (eng)):15-25.

Sherazi SWA, Zheng H, Lee JYJS. A machine learning-based applied prediction model for identification of acute coronary syndrome (ACS) outcomes and mortality in patients during the hospital stay. 2023;23(3):1351.

Shetty MK, Kunal S, Girish M, Qamar A, Arora S, Hendrickson M, et al. Machine learning based model for risk prediction after ST-Elevation myocardial infarction: Insights from the North India ST elevation myocardial infarction (NORIN-STEMI) registry. 2022;362:6-13.

Song T, Qu XF, Zhang YT, Cao W, Han BH, Li Y, et al. Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction. 2014;14(1):59.

Sritharan HP, Nguyen H, Ciofani J, Bhindi R, Allahwala UKJFiCM. Machine-learning based risk prediction of in-hospital outcomes following STEMI: the STEMI-ML score. 2024;11:1454321.

Tang N, Liu S, Li K, Zhou Q, Dai Y, Sun H, et al. Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods. 2024;11:1419551.

Tarabanis C, Kalampokis E, Khalil M, Alviar CL, Chinitz LA, Jankelson LJCDHJ. Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction. 2023;4(4):126-32.

Tindale A, Cretu I, Meng H, Panoulas VJEHJACC. Complete revascularization is associated with higher mortality in patients with ST-elevation myocardial infarction, multi-vessel disease and shock defined by hyperlactataemia: results from the Harefield Shock Registry incorporating explainable machine learning. 2023;12(9):615-23.

Tsarapatsani K-H, Sakellarios A, Tsakanikas VD, Kleber M, Marz W, Fotiadis DI, editors. Machine learning models predict fatal myocardial infarction within 10-years follow-up utilizing explainable ai. 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE); 2023: IEEE.

Vazquez B, Fuentes-Pineda G, Garcia F, Borrayo G, Prohias JJIiMU. Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: a machine learning approach. 2021;27:100791.

Wallert J, Tomasoni M, Madison G, Held CJBmi, making d. Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data. 2017;17(1):99.

Wang N, Wang M, Zhou Y, Liu H, Wei L, Fei X, et al. Sequential data–based patient similarity framework for patient outcome prediction: algorithm development. 2022;24(1):e30720.

Wang Y, Li C, Yuan M, Ren B, Liu C, Zheng J, et al. Development of a complete blood count with differential—based prediction model for in-hospital mortality among patients with acute myocardial infarction in the coronary care unit. 2022;9:1001356.

Wang L, Zhang Y, Li F, Li C, Xu HJPo. Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. 2024;19(12):e0312448.

Xie P, Wang H, Xiao J, Xu F, Liu J, Chen Z, et al. Development and validation of an explainable deep learning model to predict in-hospital mortality for patients with acute myocardial infarction: algorithm development and validation study. 2024;26:e49848.

Yan M, Miao Y, Sheng S, Gan X, He B, Shen LJJoSJU. Ensemble learning-based mortality prediction after acute myocardial infarction. 2025;30(1):153-65.

Yang J, Li Y, Li X, Tao S, Zhang Y, Chen T, et al. A machine learning model for predicting In-Hospital mortality in Chinese patients with ST-Segment elevation myocardial infarction: findings from the China myocardial infarction registry. 2024;26:e50067.

Yu F, Xu Y, Peng JJSCJ. Evaluation of a nomogram model for predicting in-hospital mortality risk in patients with acute ST-elevation myocardial infarction and acute heart failure post-PCI. 2024;58(1):2387001.

Zhang Q, Xu L, Xie Z, He W, Huang XJFiCM. Machine learning-based prediction of mortality in acute myocardial infarction with cardiogenic shock. 2024;11:1402503.

Zhang Y, Wang L, Li F, Xu H, Pei S, editors. Stacking Ensemble Models Predict Mortality from Acute Myocardial Infarction. International Conference in Communications, Signal Processing, and Systems; 2023: Springer.

Zhao J, Zhao P, Li C, Hou YJT, management cr. Optimized machine learning models to predict in-hospital mortality for patients with ST-segment elevation myocardial infarction. 2021:951-61.

Zhao P, Liu C, Zhang C, Hou Y, Zhang X, Zhao J, et al. Using machine learning to predict the in-hospital mortality in women with ST-segment elevation myocardial infarction. 2023;24(5):126.

Zheng H, Waqar MM, Arif S, Sherazi SWA, Son SH, Lee JY, editors. An Explainable Machine Learning-based Prediction Model for In-hospital Mortality in Acute Myocardial Infarction Patients with Typical Chest Pain. Proceedings of the 2023 6th International Conference on Software Engineering and Information Management; 2023.

Zheng H, Sherazi SWA, Lee JYJFiCM. A cost-sensitive deep neural network-based prediction model for the mortality in acute myocardial infarction patients with hypertension on imbalanced data. 2024;11:1276608.

Zhu X, Xie B, Chen Y, Zeng H, Hu JJCCA. Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction. 2024;554:117776.