Avinash, B. S., Srisupattarawanit, T. & Ostermeyer, H. Numerical methods for information tracking of noisy and non-smooth data in large-scale statistics. J. Eng. Res. Rep. https://doi.org/10.9734/jerr/2019/v6i416957 (2019).
Google Scholar
Zhang, J. et al. Guest editorial learning from noisy multimedia data. IEEE Trans. Multimed https://doi.org/10.1109/TMM.2022.3159014 (2022).
Google Scholar
Arain, Z., Iliodromiti, S., Slabaugh, G., David, A. L. & Chowdhury, T. T. Machine learning and disease prediction in obstetrics. Curr. Res. Physiol. https://doi.org/10.1016/j.crphys.2023.100099 (2023).
Google Scholar
Veena, S., Sumanth Reddy, D., Lakshmi Kara, C. & Uday Kiran, K. A. Clinical outcome future prediction with decision tree & naive bayes models, in Advances in Science and Technology, Vol. 124. AST (2023).
Li, Y., Fan, X., Wei, L., Yang, K. & Jiao, M. The impact of high-risk lifestyle factors on all-cause mortality in the US non-communicable disease population. BMC Public Health 23, 422 (2023).
Google Scholar
Taneri, P. E. et al. Association between ultra-processed food intake and all-cause mortality: A systematic review and meta-analysis. Am. J. Epidemiol. 191, 1323–1335 (2022).
Google Scholar
Feng, X., Sarma, H., Seubsman, S. A., Sleigh, A. & Kelly, M. The impact of multimorbidity on all-cause mortality: A longitudinal study of 87,151 thai adults. Int. J. Public Health 68, 1606137 (2023).
Google Scholar
Takahama, H. et al. Clinical application of artificial intelligence algorithm for prediction of one-year mortality in heart failure patients. Heart Vessels 38, 785–792 (2023).
Google Scholar
Arostegui, I. et al. Combining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon cancer. Clin. Epidemiol. 10, 235–251 (2018).
Google Scholar
Xiong, J. et al. A novel machine learning-based programmed cell death-related clinical diagnostic and prognostic model associated with immune infiltration in endometrial cancer. Front. Oncol. 13, 1224071 (2023).
Google Scholar
Wang, G. et al. Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism. BMC Cardiovasc. Disord. 23, 385 (2023).
Google Scholar
Bacevicius, M. & Paulauskaite-Taraseviciene, A. Machine learning algorithms for raw and unbalanced intrusion detection data in a multi-class classification problem. Appl. Sci. (Switzerland) 13, 7328 (2023).
Delpino, F. M. et al. Machine learning for predicting chronic diseases: A systematic review. Public Health 205, 14–25 (2022).
Google Scholar
Delpino, F. M. et al. Does machine learning have a high performance to predict obesity among adults and older adults? A systematic review and meta-analysis. Nutr. Metab. Cardiovasc. Dis. 34(9), 2034–2045 (2024).
Google Scholar
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D. & Tzovara, A. Addressing bias in big data and AI for health care: A call for open science. Patterns https://doi.org/10.1016/j.patter.2021.100347 (2021).
Google Scholar
Matthew, P. et al. PRISMA 2020 statement: updated guidelines for reporting systematic reviews and meta analyses. 26th Cochrane Colloquium Santiago Chile (2019).
Collins, G. S. et al. TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).
Google Scholar
Meurer, W. J. & Tolles, J. Logistic regression diagnostics understanding how well a model predicts outcomes. JAMA J. Am. Med. Assoc. https://doi.org/10.1001/jama.2016.20441 (2017).
Google Scholar
7.7.7.2 Standard errors from confidence intervals and P values: difference measures. https://handbook-5-1.cochrane.org/chapter_7/7_7_7_2_obtaining_standard_errors_from_confidence_intervals_and.htm.
Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982).
Google Scholar
Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1, 97–111 (2010).
Google Scholar
Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. Introduction to meta-analysis. Introd. Meta-Anal. https://doi.org/10.1002/9780470743386 (2009).
Google Scholar
Heyman, E. T. et al. Improving machine learning 30-day mortality prediction by discounting surprising deaths. J. Emerg. Med. 61, 763–773 (2021).
Google Scholar
Bergquist, T. et al. Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine. J. Am. Med. Inform. Assoc. 31, 35–44 (2024).
Google Scholar
Díez-Sanmartín, C., Cabezuelo, A. S. & Belmonte, A. A. A new approach to predicting mortality in dialysis patients using sociodemographic features based on artificial intelligence. Artif. Intell. Med. 136, 102478 (2023).
Google Scholar
Siegersma, K. R. et al. Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk. Eur. Heart J. Digit. Health 3, 245–254 (2022).
Google Scholar
Barsasella, D. et al. A machine learning model to predict length of stay and mortality among diabetes and hypertension inpatients. Medicina (Kaunas) 58, 1568 (2022).
Google Scholar
Li, D., Fu, J., Zhao, J., Qin, J. & Zhang, L. A deep learning system for heart failure mortality prediction. PLoS ONE 18, e0276835 (2023).
Google Scholar
Tedesco, S. et al. Comparison of machine learning techniques for mortality prediction in a prospective cohort of older adults. Int. J. Environ. Res. Public Health 18, 12806 (2021).
Google Scholar
Tang, W. H. W. et al. Prognostic value of baseline and changes in circulating soluble ST2 levels and the effects of nesiritide in acute decompensated heart failure. JACC Heart Fail. 4, 68–77 (2016).
Google Scholar
Shah, N. D., Steyerberg, E. W. & Kent, D. M. Big data and predictive analytics: Recalibrating expectations. JAMA J. Am. Med. Assoc. https://doi.org/10.1001/jama.2018.56024 (2018).
Google Scholar
Zerillo, J. A. et al. An international collaborative standardizing a comprehensive patient-centered outcomes measurement set for colorectal cancer. JAMA Oncol https://doi.org/10.1001/jamaoncol.2017.0417 (2017).
Google Scholar
Wiens, J., Guttag, J. & Horvitz, E. Patient risk stratification with time-varying parameters: A multitask learning approach. J. Mach. Learn. Res. 17, 1–23 (2016).
Google Scholar
Rieke, N. et al. The future of digital health with federated learning. NPJ Digit. Med. 3, 119 (2020).
Google Scholar
Chen, I. Y., Szolovits, P. & Ghassemi, M. Can AI help reduce disparities in general medical and mental health care?. AMA J. Ethics 21, 167–179 (2019).
Google Scholar
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G. & Chin, M. H. Ensuring fairness in machine learning to advance health equity. Ann. Intern. Med. 169, 866–872 (2018).
Google Scholar
Inthout, J., Ioannidis, J. P. A., Borm, G. F. & Goeman, J. J. Small studies are more heterogeneous than large ones: A meta-meta-analysis. J. Clin. Epidemiol. 68, 860–869 (2015).
Google Scholar
Resche-Rigon, M., White, I. R., Bartlett, J. W., Peters, S. A. E. & Thompson, S. G. Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data. Stat. Med. 32, 4890 (2013).
Google Scholar
Chen, X. et al. Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis. Lancet Glob. Health 9, e598 (2021).
Google Scholar
Xie, Z., Ding, J., Jiao, J., Tang, S. & Huang, C. Screening instruments for early identification of unmet palliative care needs: a systematic review and meta-analysis. BMJ Support Palliat. Care 14, 256 (2024).
Google Scholar
Issitt, R. W. et al. Classification performance of neural networks versus logistic regression models: Evidence from healthcare practice. Cureus 14, e22443 (2022).
Google Scholar
Sculley, D. et al. Hidden technical debt in machine learning systems, in Advances in Neural Information Processing Systems, Vol. 2015-January (2015).
Mohammed, S. et al. The effects of data quality on machine learning performance on tabular data. Inf. Syst. 132, 102549 (2025).
Google Scholar
Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. https://doi.org/10.1038/s42256-019-0048-x (2019).
Google Scholar
Rudin, C. et al. Interpretable machine learning: Fundamental principles and 10 grand challenges. Stat. Surv. 16, 1–85 (2022).
Google Scholar
Amann, J., Blasimme, A., Vayena, E., Frey, D. & Madai, V. I. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med. Inform. Decis. Mak. 20, 310 (2020).
Google Scholar
Wichmann, R. M. et al. Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts. Sci. Rep. 13, 1022 (2023).
Google Scholar
Ueda, D. et al. Fairness of artificial intelligence in healthcare: Review and recommendations. Jpn. J. Radiol. https://doi.org/10.1007/s11604-023-01474-3 (2024).
Google Scholar
Banerjee, S., Lio, P., Jones, P. B. & Cardinal, R. N. A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness. NPJ Schizophr. 7, 60 (2021).
Google Scholar
Meredith, J. W. et al. A comparison of the abilities of nine scoring algorithms in predicting mortality. J. Trauma 53, 621 (2002).
Google Scholar
Wang, Y. et al. A maintenance hemodialysis mortality prediction model based on anomaly detection using longitudinal hemodialysis data. J. Biomed. Inform. 123, 103930 (2021).
Google Scholar
Lin, S. Y. et al. Artificial intelligence prediction model for the cost and mortality of renal replacement therapy in aged and super-aged populations in Taiwan. J. Clin. Med. 8, 995 (2019).
Google Scholar
Liu, C. M. et al. Artificial intelligence-enabled model for early detection of left ventricular hypertrophy and mortality prediction in young to middle-aged adults. Circ. Cardiovasc. Qual. Outcomes 15, e008360 (2022).
Google Scholar
Shi, H. Y. et al. Artificial neural network model for predicting 5-year mortality after surgery for hepatocellular carcinoma: A nationwide study. J. Gastrointest. Surg. 16, 2126 (2012).
Google Scholar
Shi, L., Wang, X. C. & Wang, Y. S. Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China. Braz. J. Med. Biol. Res. 46, 993 (2013).
Google Scholar
Puddu, P. E. & Menotti, A. Artificial neural networks versus proportional hazards Cox models to predict 45-year all-cause mortality in the Italian Rural Areas of the Seven Countries Study. BMC Med. Res. Methodol. 12, 100 (2012).
Google Scholar
Harris, A. H. S. et al. Can machine learning methods produce accurate and easy-to-use prediction models of 30-day complications and mortality after knee or hip arthroplasty?. Clin. Orthop. Relat. Res. 477, 452 (2019).
Google Scholar
Jing, B. et al. Comparing machine learning to regression methods for mortality prediction using veterans affairs electronic health record clinical data. Med. Care 60, 470 (2022).
Google Scholar
Sakr, S. et al. Comparison of machine learning techniques to predict all-cause mortality using fitness data: The Henry Ford exercIse testing (FIT) project. BMC Med. Inform. Decis. Mak. 17, 174 (2017).
Google Scholar
Jones, B. E. et al. Computerized mortality prediction for community-acquired pneumonia at 117 veterans affairs medical centers. Ann. Am. Thorac. Soc. 18, 1175 (2021).
Google Scholar
Singh, A. et al. Deep learning for explainable estimation of mortality risk from myocardial positron emission tomography images. Circ. Cardiovasc. Imaging 15, e014526 (2022).
Google Scholar
Lu, M. T. et al. Deep learning to assess long-term mortality from chest radiographs. JAMA Netw .Open 2, e197416 (2019).
Google Scholar
Ulloa Cerna, A. E. et al. Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality. Nat. Biomed. Eng. 5, 546 (2021).
Google Scholar
Wang, L. et al. Development and validation of a deep learning algorithm for mortality prediction in selecting patients with dementia for earlier palliative care interventions. JAMA Netw. Open 2, e196972 (2019).
Google Scholar
Mohammad, M. A. 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. Lancet Digit. Health 4, e37 (2022).
Google Scholar
Valsaraj, A. et al. Development and validation of echocardiography-based machine-learning models to predict mortality. EBioMedicine 90, 104479 (2023).
Google Scholar
Li, Z. et al. Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China. Front. Public Health 11, 1033070 (2023).
Google Scholar
Zhou, J. et al. Development of an electronic frailty index for predicting mortality and complications analysis in pulmonary hypertension using random survival forest model. Front. Cardiovasc. Med. 9, 735906 (2022).
Google Scholar
Giang, K. W., Helgadottir, S., Dellborg, M., Volpe, G. & Mandalenakis, Z. Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register-based medical hospital data and neural networks. Eur. Heart J. Digit. Health 2, 568 (2021).
Google Scholar
Castela Forte, J. et al. Ensemble machine learning prediction and variable importance analysis of 5-year mortality after cardiac valve and CABG operations. Sci. Rep. 11, 3467 (2021).
Google Scholar
Hernesniemi, J. A. et al. Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome–the MADDEC study. Ann. Med. 51, 156 (2019).
Google Scholar
Qiu, W. et al. Interpretable machine learning prediction of all-cause mortality. Commun. Med. 2, 125 (2022).
Google Scholar
Niedziela, J. T. et al. Is neural network better than logistic regression in death prediction in patients after ST-segment elevation myocardial infarction?. Kardiol. Pol. 79, 1353 (2021).
Google Scholar
Mostafaei, S. et al. Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: A longitudinal cohort study. Sci. Rep. 13, 9480 (2023).
Google Scholar
Cui, Y. et al. Machine learning approaches for prediction of early death among lung cancer patients with bone metastases using routine clinical characteristics: An analysis of 19,887 patients. Front. Public Health 10, 1019168 (2022).
Google Scholar
Parikh, R. B. et al. Machine learning approaches to predict 6-month mortality among patients with cancer. JAMA Netw. Open 2, 1019168 (2019).
Google Scholar
Tong, J. et al. Machine learning can predict total death after radiofrequency ablation in liver cancer patients. Clin. Med. Insights Oncol. 15, 11795549211000016 (2021).
Google Scholar
de Capretz, P. O. et al. Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation. BMC Med. Inform. Decis. Mak. 23, 25 (2023).
Google Scholar
Tian, P. et al. Machine learning for mortality prediction in patients with heart failure with mildly reduced ejection fraction. J. Am. Heart Assoc. 12, e029124 (2023).
Google Scholar
Mamprin, M. et al. Machine learning for predicting mortality in transcatheter aortic valve implantation: An inter-center cross validation study. J. Cardiovasc. Dev. Dis. 8, 65 (2021).
Google Scholar
Motwani, M. et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: A 5-year multicentre prospective registry analysis. Eur. Heart J. 38, 500–507 (2017).
Google Scholar
dos Santos, H. G., do Nascimento, C. F., Izbicki, R., de Duarte, Y. A. O. & Filho, A. D. P. C. Machine learning for predictive analyses in health: An example of an application to predict death in the elderly in São Paulo, Brazil. Cad Saude Publica 35, e00050818 (2019).
Google Scholar
Feng, X. et al. Machine learning improves mortality prediction in three-vessel disease. Atherosclerosis 367, 1–7 (2023).
Google Scholar
Yu, Y. et al. Machine learning methods for predicting long-term mortality in patients after cardiac surgery. Front. Cardiovasc. Med. 9, 831390 (2022).
Google Scholar
Tamminen, J., Kallonen, A., Hoppu, S. & Kalliomäki, J. Machine learning model predicts short-term mortality among prehospital patients: A prospective development study from Finland. Resusc. Plus 5, 100089 (2021).
Google Scholar
Xu, C., Subbiah, I. M., Lu, S. C., Pfob, A. & Sidey-Gibbons, C. Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data. Qual. Life Res. 32, 713 (2023).
Google Scholar
Katsiferis, A. et al. Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals. PLoS ONE 18, e0289632 (2023).
Google Scholar
Kanda, E. et al. Machine learning models predicting cardiovascular and renal outcomes and mortality in patients with hyperkalemia. Nutrients 14, 4614 (2022).
Google Scholar
Lu, J. et al. Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data. Sci. Rep. 11, 18314 (2021).
Google Scholar
Scrutinio, D. et al. Machine learning to predict mortality after rehabilitation among patients with severe stroke. Sci. Rep. 10, 20127 (2020).
Google Scholar
Li, Y. M. et al. Machine learning to predict the 1-year mortality rate after acute anterior myocardial infarction in Chinese patients. Ther. Clin. Risk Manag. 16, 1–16 (2020).
Google Scholar
Guo, R. et al. Machine learning-based approaches for prediction of patients’ functional outcome and mortality after spontaneous intracerebral hemorrhage. J. Pers. Med. 12, 112 (2022).
Google Scholar
Li, Y. et al. Machine learning-based models to predict one-year mortality among Chinese older patients with coronary artery disease combined with impaired glucose tolerance or diabetes mellitus. Cardiovasc. Diabetol. 22, 139 (2023).
Google Scholar
Asrian, G., Suri, A. & Rajapakse, C. Machine learning-based mortality prediction in hip fracture patients using biomarkers. J. Orthop. Res. 42, 395 (2024).
Google Scholar
Ivanics, T. et al. Machine learning–based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries. Am. J. Transpl. 23, 64 (2023).
Google Scholar
Behnoush, A. H. et al. Machine learning-based prediction of 1-year mortality in hypertensive patients undergoing coronary revascularization surgery. Clin. Cardiol. 46, 269 (2023).
Google Scholar
Kampaktsis, P. N. et al. Machine learning-based prediction of mortality after heart transplantation in adults with congenital heart disease: A UNOS database analysis. Clin. Transpl. 37, e14845 (2023).
Google Scholar
Lin, Y. J. et al. Machine-learning monitoring system for predicting mortality among patients with noncancer end-stage liver disease: Retrospective study. JMIR Med. Inform. 8, e24305 (2020).
Google Scholar
Lin, F. Y. et al. Mortality impact of low CAC density predominantly occurs in early atherosclerosis: Explainable ML in the CAC consortium. J. Cardiovasc. Comput. Tomogr. 17, 28 (2023).
Google Scholar
Liu, Y. et al. Nomogram and machine learning models predict 1-year mortality risk in patients with sepsis-induced cardiorenal syndrome. Front. Med. (Lausanne) 9, 792238 (2022).
Google Scholar
Forssten, M. P., Bass, G. A., Ismail, A. M., Mohseni, S. & Cao, Y. Predicting 1-year mortality after hip fracture surgery: An evaluation of multiple machine learning approaches. J. Pers. Med. 11, 727 (2021).
Google Scholar
Alimbayev, A. et al. Predicting 1-year mortality of patients with diabetes mellitus in Kazakhstan based on administrative health data using machine learning. Sci. Rep. 13, 8412 (2023).
Google Scholar
El-Bouri, W. K., Sanders, A. & Lip, G. Y. H. Predicting acute and long-term mortality in a cohort of pulmonary embolism patients using machine learning. Eur. J. Intern. Med. 118, 42 (2023).
Google Scholar
Park, J. et al. Predicting long-term mortality in patients with acute heart failure by using machine learning. J. Card Fail. 28, 1078 (2022).
Google Scholar
Penso, M. et al. Predicting long-term mortality in TAVI patients using machine learning techniques. J. Cardiovasc. Dev. Dis. 8, 44 (2021).
Google Scholar
Guo, A., Mazumder, N. R., Ladner, D. P. & Foraker, R. E. Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning. PLoS ONE 16, e0256428 (2021).
Google Scholar
Abedi, V. et al. Predicting short and long-term mortality after acute ischemic stroke using EHR. J. Neurol. Sci. 427, 117560 (2021).
Google Scholar
Zhou, J. et al. Predicting stroke and mortality in mitral regurgitation: A machine learning approach. Curr. Probl. Cardiol. https://doi.org/10.1016/j.cpcardiol.2022.101464 (2023).
Google Scholar
Rauf, A. et al. Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach. Ann. Noninvasive Electrocardiol. 28, e13078 (2023).
Google Scholar
Shi, N. et al. Predicting the need for therapeutic intervention and mortality in acute pancreatitis: A two-center international study using machine learning. J. Pers. Med. 12, 616 (2022).
Google Scholar
Zhou, Y. et al. Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China. Int. J. Cardiol. 339, 21 (2021).
Google Scholar
Lee, H. C. et al. Prediction of 1-year mortality from acute myocardial infarction using machine learning. Am. J. Cardiol. 133, 23 (2020).
Google Scholar
Tran, N. T. D. et al. Prediction of all-cause mortality for chronic kidney disease patients using four models of machine learning. Nephrol. Dial. Transpl. 38, 1691 (2023).
Google Scholar
Raghunath, S. et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat. Med. 26, 886 (2020).
Google Scholar
Kawano, K. et al. Prediction of mortality risk of health checkup participants using machine learning-based models: The J-SHC study. Sci. Rep. 12, 141546 (2022).
Google Scholar
Weng, S. F., Vaz, L., Qureshi, N. & Kai, J. Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLoS ONE 14, e0214365 (2019).
Google Scholar
Zhou, Q. et al. Prediction of premature all-cause mortality in patients receiving peritoneal dialysis using modified artificial neural networks. Aging 13, 14170 (2021).
Google Scholar
Huang, S. H., Loh, J. K., Tsai, J. T., Houg, M. F. & Shi, H. Y. Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents. Chin. J. Cancer 36, 1–9 (2017).
Google Scholar
Sheng, K. et al. Prognostic machine learning models for first-year mortality in incident hemodialysis patients: Development and validation study. JMIR Med. Inform. 8, e20578 (2020).
Google Scholar
Zachariah, F. J., Rossi, L. A., Roberts, L. M. & Bosserman, L. D. Prospective comparison of medical oncologists and a machine learning model to predict 3-month mortality in patients with metastatic solid tumors. JAMA Netw. Open https://doi.org/10.1001/jamanetworkopen.2022.14514 (2022).
Google Scholar
Unterhuber, M. et al. Proteomics-enabled deep learning machine algorithms can enhance prediction of mortality. J. Am. Coll. Cardiol. 78, 1621 (2021).
Google Scholar
Wu, X. D. et al. Risk factors prediction of 6-month mortality after noncardiac surgery of older patients in China: A multicentre retrospective cohort study. Int. J. Surg. 110, 219 (2024).
Google Scholar
Hwangbo, L. et al. Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients. Sci. Rep. 12, 17389 (2022).
Google Scholar
Ross, E. G. et al. The use of machine learning for the identification of peripheral artery disease and future mortality risk. J. Vasc. Surg. 64, 1515 (2016).
Google Scholar
Wang, H. et al. Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk, in Studies in Health Technology and Informatics, Vol. 264 (2019).
