Analysis of Machine Learning Techniques for Heart Failure Readmissions

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

Regularization Paths for Generalized Linear Models via Coordinate Descent

Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927

Classification and Regression by randomForest

Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594

pROC: an open-source package for R and S+ to analyze and compare ROC curves

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697

Recognizing human actions: a local SVM approach

Schüldt, C., Laptev, I., & Barbara Caputo, B. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334462 · Cited by 3,248

Gene selection and classification of microarray data using random forest

Díaz‐Uriarte, R., & de Andrés, S. Á. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-3 · Cited by 3,013

Recognizing human actions: A local SVM approach

Schüldt, C., Laptev, I., & Barbara Caputo, B. (2004) · Cited by 2,908

Big Data in Cardiovascular Disease

Lima, F. V., Russell, R. R., & Druz, R. S. (2019) · Current Epidemiology Reports · doi:10.1007/s40471-019-00209-1 · Cited by 1

Using Data Science to Predict Readmissions in Heart Failure

Apakama, D. U., & Slovis, B. H. (2019) · Current Emergency and Hospital Medicine Reports · doi:10.1007/s40138-019-00197-y · Cited by 1

CLUE: Personalized Hospital Readmission Prediction Against Data Insufficiency under Imbalanced-Data Environment

Meng, Q., Cui, L., Yu, G., Yu, H., Guo, W., & Li, H. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313562 · Cited by 1

Hypertensive Retinopathy Classification Using Improved Clustering Algorithm and the Improved Convolution Neural Network

Usharani, B. (2021) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-7998-8161-2.ch007 · Cited by 1

Artificial Intelligence in Pediatric Cardiology

Gearhart, A. S., & Chang, A. C. (2022) · Springer eBooks · doi:10.1007/978-3-030-88892-3_27 · Cited by 1

Prediction of Diabetic Patient Readmission Using Machine Learning

Ramírez, J. C., & Herrera, D. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-36211-9_7 · Cited by 1

Semantic-based Attention model for Hospital Readmission Prediction

Im, S. J., Xu, Y., & Watson, J. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020315 · Cited by 2

Risk prediction with office and ambulatory blood pressure using artificial intelligencePreprint

Guimarães, P., Keller, A., Böhm, M., Lauder, L., Ayala, J. L., Banegas, J. R., de la Sierra, A., Vinyoles, E., Gorostidi, M., Segura, J., et al. (2020) · medRxiv · doi:10.1101/2020.01.17.20017798 · Cited by 1

30-days all-cause prediction model for readmissions for heart failure patients a comparative study of machine learning approaches

Bukhari, A. A. (2019) · doi:10.17760/d20327421 · Cited by 1

Machine Learning Techniques for Predicting and Managing Heart Failure

Plati, D. K., Tripoliti, E. E., Karanasiou, G. S., Rammos, A., Bechlioulis, A., Watson, C. J., McDonald, K., Ledwidge, M. T., Goletsis, Y., Naka, K. Κ., et al. (2022) · Wiley eBooks · doi:10.1002/9781119813040.ch9 · Cited by 1

Examining the Efficacies of Different Machine Learning Algorithms on Predicting Future Potential Death from Heart FailurePreprint

Radi, O. (2023) · medRxiv · doi:10.1101/2023.11.11.23298416 · Cited by 1

Predictive Analytics in Heart Failure Prediction: A Systematic ReviewPreprint

Hidayaturrohman, Q. A., & Hanada, E. (2024) · Preprints.org · doi:10.20944/preprints202401.1671.v1 · Cited by 1

Predicting 30-Day Mortality and Readmission Using Hospital Discharge Summaries: A Comparative Analysis of Machine Learning Models, Large Language Models, and PhysiciansPreprint

Scarlat, A., & Campion, F. X. (2025) · medRxiv · doi:10.1101/2025.03.26.25324714 · Cited by 1

Interpretable Hazard Models Reveal Strong Metastasis Dependence and Feature Interaction Effects in Predicting Cancer Patient Readmission RiskPreprint

Liu, S., Li, X., & Hu, Z. (2025) · medRxiv · doi:10.1101/2025.06.23.25330159 · Cited by 1

Télémédecine dans le domaine de l’insuffisance cardiaque

Andrèsa, E., Talha, S., Hajjam, M., Zulfiqar, A., Hajjam, J., Ervé, S., & Hassani, A. H. E. (2019) · Les Tribunes de la santé · doi:10.3917/seve1.060.0037

Percutaneous Left Atrial Appendage Closure: Incidence, Causes and Predictors of In-Hospital Outcomes and Readmissions a United States Nationwide Retrospective Cohort Study Using Claims Data

Vuddanda, V. L. K. (2019) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)

Predicting Stroke and Mortality in Mitral Regurgitation: A Gradient Boosting ApproachPreprint

Zhou, J., Lee, S., Liu, Y., Liu, T., Tse, G., & Zhang, Q. (2021) · medRxiv · doi:10.1101/2021.01.04.21249215

Development and Validation of Interpretable Machine Learning for Stroke Occurrence in Older, Community Chinese DwellersPreprint

Wu, Y., Jiang, Z., Lin, S., & Fang, Y. (2021) · Research Square · doi:10.21203/rs.3.rs-604690/v1

New Insights on Implementing and Evaluating Artificial Intelligence in Cardiovascular Care

Dykstra, S., White, J. A., & Gavrilova, M. L. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-79161-2_17

An Advanced Machine Learning Method for Simultaneous Breast Cancer Risk Prediction and Risk Factors’ Significance Ranking in Chinese PopulationPreprint

Liu, L., He, Y., Kao, C., Fan, Y., Yang, F., Wang, F., Yu, L., Zhou, F., Xiang, Y., Huang, S., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4022247

Magnetic Resonance Imaging-Based Coronary Flow: The Role of Artificial Intelligence

Passerini, T., Yang, Y., Chițiboi, T., & Oshinski, J. N. (2022) · Contemporary medical imaging · doi:10.1007/978-3-030-92087-6_35

Development and Validation of Interpretable Machine Learning Approaches for Early Identification of Stroke in Older, Community Dwellers (Preprint)Preprint

Wu, Y., Jiang, Z., Lin, S., & Fang, Y. (2021) · doi:10.2196/preprints.28182

Classification of hypertension using an improved unsupervised learning technique and image processing

Usharani, B., & Mittal, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99864-2.00015-9

Derivation and Validation of a Machine Learning-Based Risk Prediction Model for In-Hospital Mortality in Patients with Acute Heart Failure

Misumi, K., Matsue, Y., Nogi, K., Kagiyama, N., Kasai, T., Kitai, T., Oishi, S., Akiyama, E., Suzuki, S., Yamamoto, M., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac544.1083

Hybrid Al Framework for Remote Patient Heart Failure Risk Prediction

Xu, W., Zhao, J., Parvataneni, K., Zhang, V., Lin, S., & Wang, H. (2022) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-yantai55411.2022.9941764

Clinical applications of machine learning in heart failure

Li, X., Lee, S., Bazoukis, G., Tse, G., & Liu, T. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-819872-8.00017-3

Heart failure prediction using machine learning technique

Naser, M., Radaideh, H., Alquran, H., Mustafa, W. A., Jamlos, M. A., & Idrus, S. Z. S. (2023) · AIP conference proceedings · doi:10.1063/5.0128857

Results of the Experimentation of a Platform for Automated Detection of Situations at Risk of Cardiac Decompensation (E-CarePlatform) in Elderly Patients with Multiple Comorbidities

Andrès, E., Talha, S., Hajjam, M., Keller, O., Hajjam, J., Ervé, S., & Hajjam, A. (2018) · Archives of Cardiology and Cardiovascular Diseases · doi:10.22259/2638-4744.0101001

Artificial intelligence in heart failure

Alkhatib, D. A., & Jefferies, J. L. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00053-6

Examining the Efficacies of Different Machine Learning Algorithms on Predicting Future Death from Heart FailurePreprint

Radi, O. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4870584

Developing a Predictive Machine Learning Model to Determine the Likelihood of Readmission of Substance Abuse Patients in Zimbabwe

Museza, N. A., & Mazambani, G. (2023) · Zimbabwe Conference of Information and Communication Technologies (ZCICT) · doi:10.1109/zcict59466.2023.10528432

Deep Learning Model for Diagnosis of Chronic Diseases

Onunwor, A. C., Matthias, D., & Bennett, E. O. (2024) · IJARCCE · doi:10.17148/ijarcce.2024.13724

A Review on Data Analytical Techniques for Predicting HIV Treatment Outcome Using Selected Biomarkers

Adeyemo, J. O., & Subramaniam, P. R. (2024) · International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) · doi:10.1109/seb4sdg60871.2024.10630243

Detection of CKD Status using Feature Selection Approach based on Random Forest Classifier

Mathai, S. C. (2024) · Communications on Applied Nonlinear Analysis · doi:10.52783/cana.v31.1241

Personalized risk prediction of mortality and rehospitalization for heart failure in patients undergoing mitral valve repair surgery

Zhou, N., Zhang, K., Qiao, B., Chen, C., Guo, X., Fu, W., Zheng, J., Du, J., & Dong, R. (2024) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2024.1470987

Graph Autoencoder and StrNN based Causal Analysis of Mortality in Heart Failure PatientsPreprint

Kim, D. W. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.11.622921

Діагностика серцевих захворювань методами глибинного навчання

Petrynyak, Y. І., & Пірко, І. Б. (2024) · Науковий вісник НЛТУ України · doi:10.36930/40340711

Heart Failure Prediction: Machine Learning Application in Critical Care

Sharma, H., Sharma, G., Sharma, S., Mishra, A., Singh, A. K., & Sharma, H. (2024) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds64841.2024.00045

Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning

Mwanga, D. M., Kipchirchir, I. C., Muhua, G. O., Newton, C. R. J. C., Kadengye, D. T., Junior, A., Akpalu, A., Sen, A., Mmbando, B., Newton, C. R., et al. (2025) · Global Epidemiology · doi:10.1016/j.gloepi.2025.100183

Enhancing the heart failure survival prediction by using artificial intelligence

Ayesha, & Farooq, M. K. (2025) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2025.2459295

Machine Learning Prediction of Heart Health Status from Mobile PPG Sensor Data

Álvarez, S. A., Jurgens, C. Y., Lam, Y. K., Cummins, E., Massaro, J. C., Soydara, C., Lee, C. S., & Konstam, M. A. (2025) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms65348.2025.00086

Machine learning approaches for predicting heart failure readmissions

Pikatza-Huerga, A., Almeida, A., López, R. Q., Larrea, N., Legarreta, M. J., Zulaika, U., García, R., & García‐Gutiérrez, S. (2025) · Postgraduate Medical Journal · doi:10.1093/postmj/qgaf102

Hypertension artérielle et intelligence artificielle : vers une révolution diagnostique et thérapeutique

Lequeux, B., & Garban, T. (2025) · La Presse Médicale Formation · doi:10.1016/j.lpmfor.2025.100689

Empowering Heart Failure Management Through Artificial Intelligence

Levra, A. G. (2025) · Springer eBooks · doi:10.1007/978-3-031-95256-2_10 · Cited by 1

Preprocedural Risk Factors of In‐Hospital Mortality Following Percutaneous Coronary Intervention: A Systematic Review of Risk‐Adjustment Models

Chowdhury, M. R. K., Stub, D. A., Dinh, D. T., Chowdhury, H. A., Billah, B., & Karim, M. N. (2025) · Journal of Interventional Cardiology · doi:10.1155/joic/8760459

Readmission after Heart Failure Hospitalization: An Environmental Scan of Alberta Initiatives and OutcomesPreprint

Thieba, C., Wiebe, N., Atwood, C. E., Morley, Z., Guigue, A., Walker, R. L., Howlett, J. G., Quan, H., & Eastwood, C. A. (2025) · medRxiv · doi:10.1101/2025.09.24.25336368

Explainable Hybrid Deep Learning–Transformer Approach for Insulin Prediction

Uysal, İ. (2025) · DÜMF Mühendislik Dergisi · doi:10.24012/dumf.1663768

Optimization of Classification Models for Heart Disease: Comparison Between Feature Selection and Dimensionality Reduction Techniques

Restrepo-Parra, E., Ariza-Colpas, P., Restrepo, L. V. R., Piñeres-Melo, M. A., Acosta-Solorzano, A. C., & Urina‐Triana, M. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0985-0_10

Artificial intelligence/machine learning in the management of diabetes mellitus

Sonia, T. A., & Sharma, C. P. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30272-5.00010-x · Cited by 1

Outpatient monitoring and self-care

Kayali, S. M., Alexander, J., Rawal, A., Alkhatib, D. A., & Jefferies, J. L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13279-7.00027-4

Multi-Stage Classification Approach for Heart Failure Disease Diagnosis and Reduced Ejection Fraction Prediction

Pavić, O., Dašić, L., Blagojević, A., Geroski, T., & Filipović, N. (2025) · International Conferences on Biological Information and Biomedical Engineering · doi:10.1109/bibe66822.2025.00119

Machine learning and deep learning in internal medicine: demystifying concepts

Ramos-Ruperto, L., Mora-Delgado, J., Rodríguez‐González, A., Sicilia, M., Pardilla, M. J., Sempere, J. M., & Puchades, R. (2025) · Revista Clínica Española (English Edition) · doi:10.1016/j.rceng.2025.502412

Additional file 6 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14374671

Additional file 3 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14374662

Additional file 8 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14374677

Additional file 7 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14374674

Additional file 4 of Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · Figshare · doi:10.6084/m9.figshare.14544760

Additional file 4 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14374665

Additional file 3 of Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method

Ren, Y., Li, Y., Pan, W., Yin, D., & Du, J. (2023) · Figshare · doi:10.6084/m9.figshare.22605995

Additional file 1 of Continuous monitoring of patients in and after the acute admission ward to improve clinical pathways: study protocol for a randomized controlled trial (Optimal-AAW)

Garssen, S. H., Kant, N., Vernooij, C. A., Mauritz, G.-J., Koning, M. V., Bosch, F. H., & Doggen, C. J. M. (2023) · Figshare · doi:10.6084/m9.figshare.23519957

Additional file 5 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14374668

Additional file 4 of Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method

Ren, Y., Li, Y., Pan, W., Yin, D., & Du, J. (2023) · Figshare · doi:10.6084/m9.figshare.22605998

Additional file 1 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14374656

Additional file 1 of Development and validation of predictive models for unplanned hospitalization in the Basque Country: analyzing the variability of non-deterministic algorithms

Olza, A., Millán, E., & Rodríguez-Álvarez, M. X. (2024) · Figshare · doi:10.6084/m9.figshare.26608746

Additional file 1 of Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · Figshare · doi:10.6084/m9.figshare.14544751

Additional file 1 of Deep learning algorithms for predicting renal replacement therapy initiation in CKD patients: a retrospective cohort study

Leung, K.-C., Ng, W. W., Siu, Y.-P., Hau, A. K. C., & Lee, H.-K. (2024) · Figshare · doi:10.6084/m9.figshare.26693311

Additional file 2 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14374659

Additional file 9 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14374680

Additional file 2 of A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction

Zhang, Z., Qiu, H., Li, W., & Chen, Y. (2020) · Figshare · doi:10.6084/m9.figshare.13467271

Additional file 1 of Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach

van der Galiën, O. P., Hoekstra, R. C., Gürgöze, M. T., Manintveld, O. C., van den Bunt, M. R., Veenman, C. J., & Boersma, E. (2021) · Figshare · doi:10.6084/m9.figshare.16916487

Additional file 3 of Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · Figshare · doi:10.6084/m9.figshare.14544757

Additional file 1 of Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data

Radhachandran, A., Garikipati, A., Zelin, N. S., Pellegrini, E., Ghandian, S., Calvert, J. S., Hoffman, J. L., Mao, Q., & Das, R. (2021) · Figshare · doi:10.6084/m9.figshare.14355343

Additional file 1 of Comparing the performance of machine learning and conventional models for predicting atherosclerotic cardiovascular disease in a general Chinese population

Fan, Z., Du, Z., Fu, J., Zhou, Y., Zhang, P., Shi, C., & Sun, Y. (2023) · Figshare · doi:10.6084/m9.figshare.23740590

Additional file 2 of Comparing the performance of machine learning and conventional models for predicting atherosclerotic cardiovascular disease in a general Chinese population

Fan, Z., Du, Z., Fu, J., Zhou, Y., Zhang, P., Shi, C., & Sun, Y. (2023) · Figshare · doi:10.6084/m9.figshare.23740593

Additional file 2 of Development and validation of predictive models for unplanned hospitalization in the Basque Country: analyzing the variability of non-deterministic algorithms

Olza, A., Millán, E., & Rodríguez-Álvarez, M. X. (2024) · Figshare · doi:10.6084/m9.figshare.26608749

Additional file 1 of A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction

Zhang, Z., Qiu, H., Li, W., & Chen, Y. (2020) · Figshare · doi:10.6084/m9.figshare.13467267

Additional file 1 of Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method

Ren, Y., Li, Y., Pan, W., Yin, D., & Du, J. (2023) · Figshare · doi:10.6084/m9.figshare.22605989

Additional file 2 of Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · Figshare · doi:10.6084/m9.figshare.14544754

Additional file 2 of Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method

Ren, Y., Li, Y., Pan, W., Yin, D., & Du, J. (2023) · Figshare · doi:10.6084/m9.figshare.22605992

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Nakamura, K., Aonuma, K., Kayser, T., Yamamoto, J., Shimizu, T., Asami, M., Sahara, N., Enomoto, Y., Hara, H., & Ikeda, T. (2025) · Circulation Reports · doi:10.1253/circrep.cr-25-0204

Using Machine Learning to Predict Adverse Events in Patients Undergoing Transcatheter Left Atrial Appendage Occlusion

Faridi, K. F., Mortazavi, B. J., Huang, S., Zimmerman, S. L., Friedman, D. J., Khera, R., Krumholz, H. M., Curtis, J. P., & Freeman, J. V. (2026) · JACC Advances · doi:10.1016/j.jacadv.2025.102540

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Sharma, V., Chawla, G. K., Kukreti, S., Ahuja, K., & Singh, S. (2026) · Exploration of Medicine · doi:10.37349/emed.2026.1001383 · Cited by 3

Condition-Specific Readmission Risk Stratification in a Predominantly Black Statewide Cohort Using Machine Learning: Development of Subtype-Specific Models for Heart Failure, Acute Myocardial Infarction, Atrial Fibrillation/Flutter, and Hypertensive Heart DiseasePreprint

Moudden, I. E., Bittner, M. L., & Dodani, S. (2026) · Research Square · doi:10.64898/2026.03.08.26347901

Advanced ML Models for a Holistic Understanding of the CVD

Majumdar, A., Vetrithangam, D., & Singh, C. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14186-6_6

Development of Artificial Intelligence Methods for the Analysis of Online Data for Medical Research Purposes : Use Case on the World Diabetes Distress Study

Ahne, A. (2022) · theses.fr (ABES) · doi:10.70675/cb6659e1z2eb6z4d45z9cb2zad33ea1144d1

Risques et bénéfices des stratégies associant anticoagulants et antiagrégants plaquettaires chez le patient coronarien

Kernéis, M. (2020) · theses.fr (ABES) · doi:10.70675/5b0bfd95zf464z4d9fz874az84f3b2cc62a9

Early detection of heart failure using trend similarity metrics combined with hybrid deep learning based on real-world health monitoring data

Zhou, C., He, C., Lin, J., He, J., Dai, P., & Wang, F. (2026) · Journal of The Royal Society Interface · doi:10.1098/rsif.2025.0997

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