Analysis of machine learning techniques for heart failure readmissions
Open accessAbstract
Background—: The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Methods and Results—: Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Conclusions—: Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates.
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AI applied to electronic patient records predicts the risk of cardiovascular disease, such as acute coronary syndrome and heart failure, better than traditional scales.
“Applied to electronic patient records, AI has been used to predict the risk of cardiovascular disease, for instance acute coronary syndrome (22) and heart failure (23) better than traditional scales.”
From Artificial Intelligence in Medicine: Today and Tomorrow (2020) · cites this paper as [23]
The Conclusions report that machine-learning methods improved prediction of readmission after heart-failure hospitalization compared with logistic regression. But the Methods describe a Tele-HF trial dataset combining medical-record abstractions with surveys and examinations, not solely electronic patient records; the outcome was readmission, not the broader risk of developing cardiovascular disease. The acute-coronary-syndrome example is attached to a different citation. (LLM, conf=0.86)
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · see citing works
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 · see citing works
Recognizing human actions: a local SVM approach
Schüldt, C., Laptev, I., & Caputo, B. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334462 · Cited by 3,022 · see citing works
Recognizing human actions: A local SVM approach
Schüldt, C., Laptev, I., & Caputo, B. (2004) · Cited by 2,927 · see citing works
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 2,924 · see citing works
Variable selection using random forests
Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.03.014 · Cited by 2,619 · see citing works
Risk Prediction Models for Hospital Readmission
Kansagara, D. L., Englander, H. L., Salanitro, A. H., Kagen, D. I., Theobald, C. N., Freeman, M., & Kripalani, S. B. (2011) · JAMA · doi:10.1001/jama.2011.1515 · Cited by 1,712 · see citing works
Menze, B. H., Kelm, B. M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W. H., & Hamprecht, F. A. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-213 · Cited by 1,315 · see citing works
Telemonitoring in patients with heart failure
Chaudhry, S. I., Mattera, J. A., Curtis, J. P., Spertus, J. A., Herrin, J., Lin, Z., Phillips, C. O., Hodshon, B. V., Cooper, L. S., & Krumholz, H. M. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1010029 · Cited by 1,232 · see citing works
Self-citationThe analysis of count data: a gentle introduction to poisson regression and its alternatives.
Coxe, S., West, S. G., & Aiken, L. S. (2009) · Journal of Personality Assessment · doi:10.1080/00223890802634175 · Cited by 1,172 · see citing works
Dharmarajan, K., Hsieh, A., Lin, Z., Bueno, H., Ross, J. S., Horwitz, L. I., Barreto-Filho, J. A. S., Kim, N. V., Bernheim, S. M., Suter, L. G., et al. (2013) · JAMA · doi:10.1001/jama.2012.216476 · Cited by 1,059 · see citing works
Self-citationCombining SVMs with Various Feature Selection Strategies
Chen, Y., & Lin, C. (2008) · Studies in fuzziness and soft computing · doi:10.1007/978-3-540-35488-8_13 · Cited by 1,008 · see citing works
Feature Selection for Classification of Hyperspectral Data by SVM
Pal, M., & Foody, G. M. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2039484 · Cited by 818 · see citing works
Mining data with random forests: A survey and results of new tests
Verikas, A., Gelžinis, A., & Bačauskienė, M. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.08.011 · Cited by 728 · see citing works
Statnikov, A., Wang, L., & Aliferis, C. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-319 · Cited by 684 · see citing works
Hospital Readmissions and the Affordable Care Act
Kocher, R. P., & Adashi, E. Y. (2011) · JAMA · doi:10.1001/jama.2011.1561 · Cited by 465 · see citing works
Net Reclassification Indices for Evaluating Risk Prediction Instruments
Kerr, K. F., Wang, Z., Janes, H., McClelland, R. L., Psaty, B. M., & PEPE, M. S. (2013) · Epidemiology · doi:10.1097/ede.0000000000000018 · Cited by 454 · see citing works
Recent National Trends in Readmission Rates After Heart Failure Hospitalization
Ross, J. S., Chen, J., Lin, Z., Bueno, H., Curtis, J. P., Keenan, P. S., Normand, S. T., Schreiner, G. C., Spertus, J. A., Vidán, M. T., et al. (2009) · Circulation Heart Failure · doi:10.1161/circheartfailure.109.885210 · Cited by 451 · see citing works
Self-citationAmarasingham, R., Moore, B. J., Tabak, Y. P., Drazner, M. H., Clark, C. A., Zhang, S., Reed, W. G., Swanson, T. S., Ma, Y., & Halm, E. A. (2010) · Medical Care · doi:10.1097/mlr.0b013e3181ef60d9 · Cited by 437 · see citing works
Risk Prediction in Patients With Heart Failure
Rahimi, K., Bennett, D., Conrad, N., Williams, T. M., Basu, J., Dwight, J., Woodward, M., Patel, A. A., McMurray, J. J. V., & MacMahon, S. W. (2014) · JACC Heart Failure · doi:10.1016/j.jchf.2014.04.008 · Cited by 412 · see citing works
Statistical Models and Patient Predictors of Readmission for Heart Failure A Systematic Review
Ross, J. S. (2008) · Archives of Internal Medicine · doi:10.1001/archinte.168.13.1371 · Cited by 343 · see citing works
Ouwerkerk, W., Voors, A. A., & Zwinderman, A. H. (2014) · JACC Heart Failure · doi:10.1016/j.jchf.2014.04.006 · Cited by 328 · see citing works
Aggregate features and ADABOOST for music classification
Bergstra, J., Casagrande, N., Erhan, D., Eck, D., & Kégl, B. (2006) · Machine Learning · doi:10.1007/s10994-006-9019-7 · Cited by 279 · see citing works
A new variable selection approach using Random Forests
Hapfelmeier, A., & Ulm, K. (2012) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2012.09.020 · Cited by 257 · see citing works
Hilden, J., & Gerds, T. A. (2013) · Statistics in Medicine · doi:10.1002/sim.5804 · Cited by 200 · see citing works
The Impact of Personalization on Smartphone-Based Activity Recognition
Weiss, G. M., & Lockhart, J. W. (2012) · Cited by 162 · see citing works
On the Consistency of Feature Selection using Greedy Least Squares Regression
Zhang, T. (2009) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 160 · see citing works
Vehicle detection combining gradient analysis and adaboost classification
Khammari, A., Nashashibi, F., Abramson, Y., & Laurgeau, C. (2005) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2005.1520202 · Cited by 146 · see citing works
Bayati, M., Braverman, M., Gillam, M., Mack, K. M., Ruiz, G., Smith, M. S., & Horvitz, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109264 · Cited by 122 · see citing works
ada: AnRPackage for Stochastic Boosting
Culp, M. V., Johnson, K., & Michailidis, G. C. (2006) · Journal of Statistical Software · doi:10.18637/jss.v017.i02 · Cited by 100 · see citing works
Randomized Trial of Telemonitoring to Improve Heart Failure Outcomes (Tele-HF): Study Design
Chaudhry, S. I., Barton, B. A., Mattera, J. A., Spertus, J. A., & Krumholz, H. M. (2007) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2007.06.720 · Cited by 81 · see citing works
Self-citationPatient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task
Wiens, J., Horvitz, E., & Guttag, J. V. (2012) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 68 · see citing works
Do Non-Clinical Factors Improve Prediction of Readmission Risk?
Krumholz, H. M., Chaudhry, S. I., Spertus, J. A., Mattera, J. A., Hodshon, B., & Herrin, J. S. (2015) · JACC Heart Failure · doi:10.1016/j.jchf.2015.07.017 · Cited by 58 · see citing works
Self-citationShape deformation: SVM regression and application to medical image segmentation
Wang, S., Zhu, W., & Liang, Z. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937626 · Cited by 52 · see citing works
Dharmarajan, K., & Krumholz, H. M. (2014) · Current Geriatrics Reports · doi:10.1007/s13670-014-0103-8 · Cited by 48 · see citing works
Self-citationWang, L., Porter, B., Maynard, C. C., Bryson, C. L., Sun, H., Lowy, E., McDonell, M. B., Frisbee, K. L., Nielson, C., & Fihn, S. D. (2012) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2012.06.038 · Cited by 39 · see citing works
New Predictive Models of Heart Failure Mortality Using Time-Series Measurements and Ensemble Models
Subramanian, D., Subramanian, V., Deswal, A. M., & Mann, D. L. (2011) · Circulation Heart Failure · doi:10.1161/circheartfailure.110.958496 · Cited by 35 · see citing works
Heart Failure Risk Prediction Models
Levy, W. C., & Anand, I. S. (2014) · JACC Heart Failure · doi:10.1016/j.jchf.2014.05.006 · Cited by 30 · see citing works
Risk Prediction Tools in Patients With Heart Failure
Lupón, J., Vila, J., & Bayés‐Genís, A. (2015) · JACC Heart Failure · doi:10.1016/j.jchf.2014.10.010 · Cited by 9 · see citing works
Multiple model recognition for near-realistic exergaming
Mortazavi, B. J., Pourhomayoun, M., Nyamathi, S., Wu, B., Lee, S. I., & Sarrafzadeh, M. (2015) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2015.7146520 · Cited by 3 · see citing works
Self-citationOpportunities and Challenges for Reducing Hospital Revisits
Dharmarajan, K., & Krumholz, H. M. (2015) · Annals of Internal Medicine · doi:10.7326/m15-0878 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Christodoulou, E., Ma, J., Collins, G. S., Steyerberg, E. W., Verbakel, J. Y., & Van Calster, B. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.02.004 · Cited by 2,119 · see citing works
Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202 · see citing works
Artificial Intelligence in Medicine: Today and Tomorrow
Briganti, G., & Le Moine, O. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00027 · Cited by 743 · see citing works
Machine learning in cardiovascular medicine: are we there yet?
Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018) · Heart · doi:10.1136/heartjnl-2017-311198 · Cited by 555 · see citing works
Al’Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., Kumar, A., Pandey, M., Maliakal, G., van Rosendael, A. R., Beecy, A. N., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehy404 · Cited by 534 · see citing works
Emergency department triage prediction of clinical outcomes using machine learning models
Raita, Y., Goto, T., Faridi, M. K., Brown, D., Camargo, C. A., & Hasegawa, K. (2019) · Critical Care · doi:10.1186/s13054-019-2351-7 · Cited by 531 · see citing works
RETRACTED ARTICLE: Artificial intelligence and machine learning in precision and genomic medicine
Quazi, S. (2022) · Medical Oncology · doi:10.1007/s12032-022-01711-1 · Cited by 455 · see citing works
RetractedWang, K., Tian, J., Zheng, C., Yang, H., Ren, J., Liu, Y., Han, Q., & Zhang, Y. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104813 · Cited by 361 · see citing works
Machine Learning for Health Services Researchers
Doupé, P., Faghmous, J. H., & Basu, S. (2019) · Value in Health · doi:10.1016/j.jval.2019.02.012 · Cited by 298 · see citing works
Angraal, S., Mortazavi, B. J., Gupta, A., Khera, R., Ahmad, T., Desai, N. R., Jacoby, D. L., Masoudi, F. A., Spertus, J. A., & Krumholz, H. M. (2019) · JACC Heart Failure · doi:10.1016/j.jchf.2019.06.013 · Cited by 297 · see citing works
Self-citationUse of Machine Learning Models to Predict Death After Acute Myocardial Infarction.
Khera, R., Haimovich, J., Hurley, N., McNamara, R., Spertus, J., Desai, N., Rumsfeld, J., Masoudi, F., Huang, C.-X., Normand, S., et al. (2021) · JAMA cardiology · doi:10.1001/jamacardio.2021.0122 · Cited by 250 · see citing works
Self-citationArtificial Intelligence in Cardiology: Present and Future
López-Jiménez, F. A., Attia, Z. I., Arruda-Olson, A. M., Carter, R. E., Chareonthaitawee, P., Jouni, H., Kapa, S., Lerman, A., Luong, C. L., Medina‐Inojosa, J. R., et al. (2020) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2020.01.038 · Cited by 288 · see citing works
Recommendations for Reporting Machine Learning Analyses in Clinical Research
Stevens, L. M., Mortazavi, B. J., Deo, R. C., Curtis, L., & Kao, D. P. (2020) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.120.006556 · Cited by 268 · see citing works
Self-citationAlaref, S. J., Maliakal, G., Singh, G., van Rosendael, A. R., Ma, X., Xu, Z., Alawamlh, O. A. H., Lee, B., Pandey, M. K., Achenbach, S. S., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz565 · Cited by 227 · see citing works
Deep Learning–Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography
Kwon, J., Lee, S. Y., Jeon, K., Lee, Y., Kim, K., Park, J., Oh, B., & Lee, M. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.014717 · Cited by 224 · see citing works
Predictive models for hospital readmission risk: A systematic review of methods
Artetxe, A., Beristain, A., & Graña, M. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.06.006 · Cited by 205 · see citing works
Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data
Rojas, J. C., Carey, K. A., Edelson, D. P., Venable, L. R., Howell, M. D., & Churpek, M. M. (2018) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201710-787oc · Cited by 177 · see citing works
Liu, T., Krentz, A., Lu, L., & Curcin, V. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae080 · Cited by 177 · see citing works
Machine learning to predict end stage kidney disease in chronic kidney disease
Bai, Q., Su, C., Tang, W., & Li, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-12316-z · Cited by 188 · see citing works
Artificial intelligence algorithm for predicting mortality of patients with acute heart failure
Kwon, J., Kim, K.-H., Jeon, K., Lee, S. E., Lee, H.-Y., Cho, H.-J., Choi, J.-O., Jeon, E.-S., Kim, M.-S., Kim, J.-J., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219302 · Cited by 140 · see citing works
Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED
Goto, T., Camargo, C. A., Faridi, M. K., Yun, B. J., & Hasegawa, K. (2018) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2018.06.062 · Cited by 164 · see citing works
Bhavnani, S. P., Parakh, K., Atreja, A., Druz, R. S., Graham, G. N., Hayek, S. S., Krumholz, H. M., Maddox, T. M., Majmudar, M. D., Rumsfeld, J. S., et al. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.10.018 · Cited by 162 · see citing works
Self-citationOutpatient Worsening Heart Failure as a Target for Therapy
Greene, S. J., Mentz, R. J., & Felker, G. M. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2017.5250 · Cited by 144 · see citing works
Sufriyana, H., Husnayain, A., Chen, Y.-L., Kuo, C.-Y., Singh, O., Yeh, T.-Y., Wu, Y., & Su, E. C. (2020) · JMIR Medical Informatics · doi:10.2196/16503 · Cited by 145 · see citing works
Awan, S. E., Bennamoun, M., Sohel, F. A., Sanfilippo, F. M., & Dwivedi, G. (2019) · ESC Heart Failure · doi:10.1002/ehf2.12419 · Cited by 137 · see citing works
Artificial intelligence in cardiology: Hope for the future and power for the present
Karatzia, L., Aung, N., & Aksentijević, D. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.945726 · Cited by 143 · see citing works
Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01284-z · Cited by 139 · see citing works
Automation, machine learning, and artificial intelligence in echocardiography: A brave new world
Gandhi, S., Mosleh, W., Shen, J., & Chow, C.-M. (2018) · Echocardiography · doi:10.1111/echo.14086 · Cited by 130 · see citing works
Krakower, D., Gruber, S., Hsu, K., Menchaca, J. T., Maro, J. C., Kruskal, B. A., Wilson, I. B., Mayer, K. H., & Klompas, M. (2019) · The Lancet HIV · doi:10.1016/s2352-3018(19)30139-0 · Cited by 125 · see citing works
Nanayakkara, S., Fogarty, S., Tremeer, M., Ross, K. J., Richards, B., Bergmeir, C., Xu, S. Y., Stub, D. A., Smith, K. L., TACEY, M. A., et al. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002709 · Cited by 117 · see citing works
Tokodi, M., Schwertner, W. R., Kovács, A. D., Tősér, Z., Staub, L., Sárkány, A., Lakatos, B. K., Behon, A., Boros, A. M., Perge, P., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz902 · Cited by 117 · see citing works
Applications of Artificial Intelligence in Cardiology. The Future is Already Here
Dorado-Díaz, P. I., Sampedro-Gómez, J., Vicente-Palacios, V., & Sánchez, P. L. (2019) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2019.05.014 · Cited by 111 · see citing works
Machine learning in heart failure
Awan, S. E., Sohel, F. A., Sanfilippo, F. M., Bennamoun, M., & Dwivedi, G. (2017) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000491 · Cited by 110 · see citing works
Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography
Kwon, J.-M., Kim, K.-H., Medina-Inojosa, J., Jeon, K.-H., Park, J., & Oh, B.-H. (2020) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2020.04.009 · Cited by 108 · see citing works
Banerjee, M., Reynolds, E. L., Andersson, H. B., & Nallamothu, B. K. (2019) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.118.004879 · Cited by 102 · see citing works
An artificial neural network approach for predicting hypertension using NHANES data
Martínez, F., Núñez‐Valdéz, E. R., Crespo, R. A. G., & García‐Díaz, V. (2020) · Scientific Reports · doi:10.1038/s41598-020-67640-z · Cited by 99 · see citing works
Inoue, T., Ichikawa, D., Ueno, T., Cheong, M., Inoue, T., Whetstone, W. D., Endo, T., Niizuma, K., & Tominaga, T. (2020) · Neurotrauma Reports · doi:10.1089/neur.2020.0009 · Cited by 99 · see citing works
Mortazavi, B. J., Bucholz, E. M., Desai, N. R., Huang, C., Curtis, J. P., Masoudi, F. A., Shaw, R. E., Negahban, S. N., & Krumholz, H. M. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.6835 · Cited by 95 · see citing works
Self-citationHan, D., Kolli, K. K., Alaref, S. J., Baskaran, L., van Rosendael, A. R., Gransar, H., Andreini, D., Budoff, M. J., Cademartiri, F., Chinnaiyan, K. M., et al. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.013958 · Cited by 97 · see citing works
Validation of deep-learning-based triage and acuity score using a large national dataset
Kwon, J., Lee, Y., Lee, Y., Lee, S., Park, H., & Park, J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205836 · Cited by 93 · see citing works
Current applications of big data and machine learning in cardiology.
Cuocolo, R., Perillo, T., Rosa, E. D., Ugga, L., & Petretta, M. (2019) · PubMed Central · doi:10.11909/j.issn.1671-5411.2019.08.002 · Cited by 95 · see citing works
Kwon, J., Kim, K., Jeon, K., & Park, J. (2018) · Echocardiography · doi:10.1111/echo.14220 · Cited by 92 · see citing works
Barakaz, F. E., Boutkhoum, O., Abdelmajid, E. M., Rustam, F., Mehmood, A., & Choi, G. S. (2021) · IEEE Access · doi:10.1109/access.2021.3056285 · Cited by 90 · see citing works
Artificial intelligence in nutrition research: perspectives on current and future applications
Côté, M., & Lamarche, B. (2021) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2021-0448 · Cited by 94 · see citing works
Huang, C.-X., Li, S.-X., Caraballo, C., Masoudi, F., Rumsfeld, J., Spertus, J., Normand, S.-L. T., Mortazavi, B., & Krumholz, H. (2021) · Circulation. Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.120.007526 · Cited by 73 · see citing works
Self-citationWang, K., Tian, J., Zheng, C., Yang, H., Ren, J. L., Li, C., Han, Q., & Zhang, Y. (2021) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s310295 · Cited by 88 · see citing works
Brufau, S. R., Wyatt, K. D., Boyum, P., Mickelson, M. M., Moore, M., & Cognetta-Rieke, C. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1715827 · Cited by 89 · see citing works
A Machine Learning Approach to Management of Heart Failure Populations
Jing, L., Ulloa, A., Good, C. W., Sauers, N. M., Schneider, G., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Hu, Y., Riviello, D., et al. (2020) · JACC Heart Failure · doi:10.1016/j.jchf.2020.01.012 · Cited by 81 · see citing works
Miller, P. E., Pawar, S., Vaccaro, B., McCullough, M., Rao, P., Ghosh, R., Warier, P., Desai, N. R., & Ahmad, T. (2019) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2019.01.018 · Cited by 81 · see citing works
Neural networks versus Logistic regression for 30 days all-cause readmission prediction
Allam, A., Nagy, M. L., Thoma, G. R., & Krauthammer, M. O. (2019) · Scientific Reports · doi:10.1038/s41598-019-45685-z · Cited by 79 · see citing works
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) · BMC Medicine · doi:10.1186/s12916-021-01940-7 · Cited by 78 · see citing works
Wingfield, L. R., Ceresa, C. D. L., Thorogood, S., Fleuriot, J., & Knight, S. R. (2020) · Liver Transplantation · doi:10.1002/lt.25772 · Cited by 77 · see citing works
Peng, S., Huang, J., Liu, X., Deng, J., Sun, C., Tang, J., Chen, H., Cao, W., Wang, W., Duan, X., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.994359 · Cited by 76 · see citing works
Luo, C., Zhu, Y., Zhu, Z., Li, R., Chen, G., & Wang, Z. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03340-8 · Cited by 74 · see citing works
Awan, S. E., Bennamoun, M., Sohel, F. A., Sanfilippo, F. M., Chow, B. J. W., & Dwivedi, G. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218760 · Cited by 70 · see citing works
Artificial Intelligence in Multiphoton Tomography: Atopic Dermatitis Diagnosis
Guimarães, P., Batista, A., Zieger, M., Kaatz, M., & Koenig, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-64937-x · Cited by 75 · see citing works
Guo, A., Pasque, M. K., Loh, F. E., Mann, D. L., & Payne, P. (2020) · Current Epidemiology Reports · doi:10.1007/s40471-020-00259-w · Cited by 72 · see citing works
Gibson, W. J., Nafee, T., Travis, R. S., Yee, M., Kernéis, M., Ohman, M. E., & Gibson, C. M. (2019) · Journal of Thrombosis and Thrombolysis · doi:10.1007/s11239-019-01940-8 · Cited by 67 · see citing works
Prediction of Incident Delirium Using a Random Forest classifier
Corradi, J., Thompson, S., Mather, J. F., Waszynski, C. M., & Dicks, R. S. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1109-0 · Cited by 66 · see citing works
Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate
Kaliappan, J., Srinivasan, K., Qaisar, S. M., Sundararajan, K., Chang, C., & Suganthan, C. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.729795 · Cited by 65 · see citing works
Artificial Intelligence: Review of Current and Future Applications in Medicine
Thomas, L. B. (2021) · Federal Practitioner · doi:10.12788/fp.0174 · Cited by 69 · see citing works
Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding
Liu, W., Singh, K., Ryan, A. M., Sukul, D., Mahmoudi, E., Waljee, A. K., Stansbury, C. M., Zhu, J., & Nallamothu, B. K. (2020) · PLoS ONE · doi:10.1371/journal.pone.0221606 · Cited by 62 · see citing works
Machine intelligence in cardiovascular medicine
Miller, D. D. (2020) · Cardiology in Review · doi:10.1097/crd.0000000000000294 · Cited by 58 · see citing works
Jiang, Y., Zhang, X., Ma, R., Wang, X., Liu, J., Keerman, M., Yan, Y., Ma, J., Song, Y., Zhang, J., et al. (2021) · Clinical Epidemiology · doi:10.2147/clep.s313343 · Cited by 59 · see citing works
Nationwide prediction of type 2 diabetes comorbidities
Dworzynski, P., Aasbrenn, M., Rostgaard, K., Melbye, M., Gerds, T., Hjalgrim, H., & Pers, T. (2019) · Scientific Reports · doi:10.1038/s41598-020-58601-7 · Cited by 51 · see citing works
Artificial Intelligence in Healthcare: Perception and Reality
Akinrinmade, A. O., Adebile, T. M., Ezuma-Ebong, C., Bolaji, K., Ajufo, A., Adigun, A. O., Mohammad, M., Dike, J. C., & Okobi, O. E. (2023) · Cureus · doi:10.7759/cureus.45594 · Cited by 70 · see citing works
Lindholm, D. P., Fukaya, E., Leeper, N. J., & Ingelsson, E. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.008970 · Cited by 56 · see citing works
Early prediction of ICU readmissions using classification algorithms
Loreto, M., Lisboa, T., & Moreira, V. P. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103636 · Cited by 53 · see citing works
Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms
Georgeanu, V. A., Mămuleanu, M., Ghiea, S., & Selișteanu, D. (2022) · Medicina · doi:10.3390/medicina58050636 · Cited by 53 · see citing works
Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble
Ali, W. B., Pesaranghader, A., Avram, R., Overtchouk, P., Perrin, N., Laffite, S., Cartier, R., Ibrahim, R., Modine, T., & Hussin, J. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.711401 · Cited by 56 · see citing works
Lorenzoni, G., Sabato, S. S., Lanera, C., Bottigliengo, D., Minto, C., Ocagli, H., Paolis, P. D., Gregori, D., Iliceto, S., & Pisanò, F. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8091298 · Cited by 50 · see citing works
Artificial intelligence in cardiovascular medicine
Ranka, S., Reddy, M., & Noheria, A. (2020) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000812 · Cited by 51 · see citing works
Sarijaloo, F. B., Park, J., Zhong, X., & Wokhlu, A. (2020) · Clinical Cardiology · doi:10.1002/clc.23532 · Cited by 50 · see citing works
Tohyama, T., Ide, T., Ikeda, M., Kaku, H., Enzan, N., Matsushima, S., Funakoshi, K., Kishimoto, J., Todaka, K., & Tsutsui, H. (2021) · ESC Heart Failure · doi:10.1002/ehf2.13556 · Cited by 47 · see citing works
Synthetic electronic health records generated with variational graph autoencoders
Nikolentzos, G., Vazirgiannis, M., Xypolopoulos, C., Lingman, M., & Brandt, E. G. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00822-x · Cited by 47 · see citing works
Hung, C.-Y., Lin, C., Lan, T., Peng, G.-S., & Lee, C.-C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213007 · Cited by 46 · see citing works
Machine learning for predicting cardiac events: what does the future hold?
Patel, B. D., & Sengupta, P. P. (2020) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2020.1732208 · Cited by 47 · see citing works
Singh, G., Hussain, Y., Xu, Z., Sholle, E., Michalak, K., Dolan, K., Lee, B. C., van Rosendael, A. R., Fatima, Z., Peña, J. M., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239934 · Cited by 45 · see citing works
Guidelines os the Brazilian Society of Cardiology on Telemedicine in Cardiology - 2019
Lopes, M. A. C. Q., de Oliveira, G. M. M., Ribeiro, A. L. P., Pinto, F. J., Rey, H. C. V., o, A. ́. A. ́. B., Zimerman, L. I., Hajjar, L. A., Rochitte, C. E., Bacal, F., et al. (2019) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20190205 · Cited by 47 · see citing works
Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure
Wehbe, R. M., Khan, S. S., Shah, S. J., & Ahmad, F. S. (2020) · Heart Failure Clinics · doi:10.1016/j.hfc.2020.05.002 · Cited by 45 · see citing works
Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning
Liu, X., Chen, Y., Bae, J. P., Li, H., Johnston, J. A., & Sanger, T. M. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983095 · Cited by 43 · see citing works
Sharma, V., Kulkarni, V., McAlister, F. A., Eurich, D. T., Keshwani, S., Simpson, S. H., Voaklander, D. C., & Samanani, S. (2021) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2021.12.004 · Cited by 42 · see citing works
Qian, X., Li, Y., Zhang, X., Guo, H., He, J., Wang, X., Yan, Y., Ma, J., Ma, R., & Guo, S. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.854287 · Cited by 41 · see citing works
Andrès, E., Talha, S., Zulfiqar, A., Hajjam, M., Ervé, S., Hajjam, J., Gény, B., & Hassani, A. H. E. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm7120544 · Cited by 42 · see citing works
Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients
Darabi, N., Hosseinichimeh, N., Noto, A., Zand, R., & Abedi, V. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.638267 · Cited by 40 · see citing works
Artificial intelligence and cardiology: Current status and perspective
Nakamura, T., & Sasano, T. (2021) · Journal of Cardiology · doi:10.1016/j.jjcc.2021.11.017 · Cited by 43 · see citing works
McGilvray, M. M. O., Heaton, J., Guo, A., Masood, M., Cupps, B. P., Damiano, M. S., Pasque, M. K., & Foraker, R. E. (2022) · JACC Heart Failure · doi:10.1016/j.jchf.2022.05.010 · Cited by 39 · see citing works
Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease
Chu, R., Chen, W., Song, G., Yao, S., Xie, L., Song, L., Zhang, Y., Chen, L., Zhang, X., Ma, Y., et al. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.016371 · Cited by 37 · see citing works
Meyer, R., Hendin, N., Zamir, M., Mor, N., Levin, G., Sivan, E., Aran, D., & Tsur, A. (2020) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.1080/14767058.2020.1837769 · Cited by 38 · see citing works
Baashar, Y. M., Alkawsi, G. A., Alhussian, H., Capretz, L. F., Alwadain, A. S., Alkahtani, A. A., & Almomani, M. A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/5849995 · Cited by 39 · see citing works
Raita, Y., Camargo, C. A., Macias, C. G., Mansbach, J. M., Piedra, P. A., Porter, S. C., Teach, S. J., & Hasegawa, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-67629-8 · Cited by 36 · see citing works
Larburu, N., Artetxe, A., Escolar, V., Lozano, A., & Kerexeta, J. (2018) · Mobile Information Systems · doi:10.1155/2018/1546210 · Cited by 35 · see citing works
Aplicaciones de la inteligencia artificial en cardiología: el futuro ya está aquí
Dorado-Díaz, P. I., Sampedro-Gómez, J., Vicente-Palacios, V., & Sanchez, P. L. (2019) · Revista Española de Cardiología · doi:10.1016/j.recesp.2019.05.016 · Cited by 36 · see citing works
Zhang, Z., Qiu, H., Li, W., & Chen, Y. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01358-w · Cited by 36 · see citing works
Prediction Model Using Machine Learning for Mortality in Patients with Heart Failure
Negassa, A., Selim, A. M., Zolty, R., & Patel, S. R. (2021) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2021.05.044 · Cited by 35 · see citing works
Hajishah, H., Kazemi, D., Safaee, E., Amini, M. J., Peisepar, M., Tanhapour, M. M., & Tavasol, A. (2025) · BMC Cardiovascular Disorders · doi:10.1186/s12872-025-04700-0 · Cited by 37 · see citing works
Lewis, M., Elad, G., Beladev, M., Maor, G., Radinsky, K., Hermann, D., Litani, Y., Geller, T. P., Pines, J. M., Shapiro, N. L., et al. (2021) · Scientific Reports · doi:10.1038/s41598-020-80856-3 · Cited by 32 · see citing works
Hurvitz, N., & Ilan, Y. (2023) · Clinics and Practice · doi:10.3390/clinpract13040089 · Cited by 32 · see citing works
Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4
Takkavatakarn, K., Oh, W., Cheng, E., Nadkarni, G. N., & Chan, L. (2023) · BMC Nephrology · doi:10.1186/s12882-023-03424-7 · Cited by 31 · see citing works
Kokori, E., Patel, R., Olatunji, G. D., Ukoaka, B. M., Abraham, I. C., Ajekiigbe, V. O., Kwape, J. M., Babalola, A. E., Udam, N. G., & Aderinto, N. (2024) · Heart Failure Reviews · doi:10.1007/s10741-024-10474-y · Cited by 31 · see citing works
- Authors
- Mortazavi, B. J., Downing, N. S., Bucholz, E. M., Dharmarajan, K., Manhapra, A., Li, S.-X., Negahban, S. N., & Krumholz, H. M.
- Affiliations
- Yale New Haven Hospital (US)
- Venue
- Circulation: Cardiovascular Quality and Outcomes
- Published
- 2016-11
- Volume
- 9
- Issue
- 6
- Pages
- 629-640
- ISSN
- 1941-7705 (ISSN-L)
- 1941-7713
- PMC
- PMC5459389
- OpenAlex
- W2549885908
- Cited by
- 401
- References
- 46
- Citations by year
- 2026: 30 · 2025: 38 · 2024: 46 · 2023: 48 · 2022: 41 · 2021: 68 · 2020: 54 · 2019: 44 · 2018: 23 · 2017: 7 · 2016: 2
- FWCI
- 43.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Circulation Cardiovascular Quality and Outcomes (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- American Federation for Aging Research
- Yale University
- National Institute on Aging (K23AG048331)
- National Heart, Lung, and Blood Institute (U01HL105270, R01HL080228)
- National Institute on Drug Abuse (R25DA033211)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- heart failure, hospital readmission prediction, machine learning, random forest, boosting, support vector machine, Poisson regression, logistic regression, all-cause readmission, heart failure readmission, remote patient monitoring, risk prediction
- MeSH terms
- Aged, Algorithms, Female, Heart Failure, Humans, Male, Middle Aged, Patient Readmission, Risk Factors, Time Factors, Reproducibility of Results, Logistic Models, Randomized Controlled Trials as Topic, Databases, Factual, Telemedicine, Nonlinear Dynamics, Risk Assessment, Data Mining, Support Vector Machine
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