Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints

R: A Language and Environment for Statistical Computing

R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works

Classification and Regression Trees.

Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works

A simulation study of the number of events per variable in logistic regression analysis

Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655 · see citing works

Regression Modeling Strategies

Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270 · see citing works

Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating

Steyerberg, E. W. (2019) · Statistics for Biology and Health · doi:10.1007/978-3-030-16399-0 · Cited by 3,281 · see citing works

Internal validation of predictive models

Steyerberg, E. W., Harrell, F. E., Borsboom, G. J. J. M., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, J. D. F. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00341-9 · Cited by 2,746 · see citing works

Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research

Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., Riley, R. D., Hemingway, H., Altman, D. G., & for the PROGRESS Group (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001381 · Cited by 1,508 · see citing works

Clinical Prediction Models

Steyerberg, E. W. (2009) · Statistics for Biology and Health · doi:10.1007/978-0-387-77244-8 · Cited by 1,491 · see citing works

Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets

Steyerberg, E. W., Eijkemans, M. J. C., Harrell, F. E., & Habbema, J. D. F. (2000) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(20000430)19:8 · Cited by 784 · see citing works

Plague: Past, Present, and Future

Stenseth, N. C., Atshabar, B. B., Begon, M. E., Belmain, S. R., Bertherat, E., Carniel, É., Gage, K. L., Leirs, H., & Rahalison, L. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050003 · Cited by 554 · see citing works

Classification and Regression Trees

Jones, E., Harden, S., & Crawley, M. J. (2022) · doi:10.1002/9781119634461.ch20 · Cited by 505 · see citing works

Data Mining and Statistics for Decision Making

Tufféry, S. (2011) · Wiley eBooks · doi:10.1002/9780470979174 · Cited by 384 · see citing works

Efficient progressive sampling

Provost, F. J., Jensen, D. D., & Oates, T. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312188 · Cited by 361 · see citing works

Assessment of Claims of Improved Prediction Beyond the Framingham Risk Score

Tzoulaki, I. (2009) · JAMA · doi:10.1001/jama.2009.1757 · Cited by 276 · see citing works

IMPACT Database of Traumatic Brain Injury: Design And Description

Marmarou, A., Lu, J., Butcher, I., McHugh, G. S., Mushkudiani, N. A., Murray, G. D., Steyerberg, E. W., & Maas, A. I. R. (2007) · Journal of Neurotrauma · doi:10.1089/neu.2006.0036 · Cited by 236 · see citing works

Data Mining and Statistics for Decision Making

Tuffry, S. (2011) · Medical Entomology and Zoology · Cited by 187 · see citing works

Static versus dynamic sampling for data mining

John, G. H., & Langley, P. (1996) · Cited by 171 · see citing works

Impact of comorbidity on short‐term mortality and overall survival of head and neck cancer patients

Datema, F. R., Ferrier, M. B., van der Schroeff, M. P., & de Jong, R. J. B. (2009) · Head & Neck · doi:10.1002/hed.21245 · Cited by 168 · see citing works

Artificial Neural Network Models for Prediction of Acute Coronary Syndromes Using Clinical Data From the Time of Presentation

Harrison, R. F., & Kennedy, R. L. (2005) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2004.09.012 · Cited by 111 · see citing works

A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients.

Selker, H. P., Griffith, J. L., Patil, S., Long, W. J., & D’Agostino, R. B. (1995) · PubMed · Cited by 101 · see citing works

Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble‐based methods?

Austin, P. C., Lee, D. S., Steyerberg, E. W., & Tu, J. V. (2012) · Biometrical Journal · doi:10.1002/bimj.201100251 · Cited by 79 · see citing works

Incidence and prediction of major cardiovascular complications in head and neck surgery

Datema, F. R., Poldermans, D., & de Jong, R. J. B. (2010) · Head & Neck · doi:10.1002/hed.21351 · Cited by 69 · see citing works

Breast cancer survival and chemotherapy: A support vector machine analysis

Lee, Y.-J., Mangasarian, O. L., & Wolberg, W. H. (2000) · DIMACS series in discrete mathematics and theoretical computer science · doi:10.1090/dimacs/055/01 · Cited by 53 · see citing works

Clinical Prediction Models: A Practical Approach to Development, Validation and Updating by STEYERBERG, E. W.

Omar, R. (2010) · Biometrics · doi:10.1111/j.1541-0420.2010.01431.x · Cited by 51 · see citing works

SAMPLE SIZE AND MODELING ACCURACY OF DECISION TREE BASED DATA MINING TOOLS

Morgan, J., Daugherty, R. A., Hilchie, A., & Carey, B. (2003) · Cited by 45 · see citing works

Requiem for large-scale models

Lee, D. B. (1975) · ACM SIGSIM Simulation Digest · doi:10.1145/1102945.1102950 · Cited by 26 · see citing works

Prediction of Survival with Alternative Modeling Techniques Using Pseudo Values

van der Ploeg, T., Datema, F. R., de Jong, R. J. B., & Steyerberg, E. W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100234 · Cited by 22 · see citing works

Developing a Prognostic Model for Traumatic Brain Injury—A Missed Opportunity?

Young, N. H., & Andrews, P. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050168 · Cited by 21 · see citing works

Prediction of intracranial findings on CT-scans by alternative modelling techniques

van der Ploeg, T., Smits, M., Dippel, D. W. J., Hunink, M. G. M., & Steyerberg, E. W. (2011) · BMC Medical Research Methodology · doi:10.1186/1471-2288-11-143 · Cited by 17 · see citing works

Untitled work

Harrell, F., Jr. (1996) · doi:10.1002/(sici)1097-0258(19960229)15:4

Untitled work

Ennis (1998) · doi:10.1002/(sici)1097-0258(19981115)17:21

Calculating the sample size required for developing a clinical prediction model

Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & van Smeden, M. (2020) · BMJ · doi:10.1136/bmj.m441 · Cited by 2,917 · see citing works

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

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

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

Moons, K. G. M., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019) · Annals of Internal Medicine · doi:10.7326/m18-1377 · Cited by 1,669 · see citing works

Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes

Riley, R. D., Snell, K. I., Ensor, J., Burke, D. L., Harrell Jr, F. E., Moons, K. G., & Collins, G. S. (2019) · Statistics in Medicine · doi:10.1002/sim.7992 · Cited by 1,409 · see citing works

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-082505 · Cited by 1,069 · see citing works

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards

Churpek, M. M., Yuen, T. C., Winslow, C., Meltzer, D. O., Kattan, M. W., & Edelson, D. P. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001571 · Cited by 638 · see citing works

Logistic regression was as good as machine learning for predicting major chronic diseases

Nusinovici, S., Tham, Y. C., Yan, M. Y. C., Ting, D. S. W., Li, J., Sabanayagam, C., Wong, T. Y., & Cheng, C. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.03.002 · Cited by 569 · see citing works

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

Goldstein, B. A., Návar, A. M., & Carter, R. E. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw302 · Cited by 551 · see citing works

Developing clinical prediction models: a step-by-step guide

Efthimiou, O., Seo, M., Chalkou, K., Debray, T., Egger, M., & Salanti, G. (2024) · BMJ · doi:10.1136/bmj-2023-078276 · Cited by 544 · see citing works

Review and evaluation of penalised regression methods for risk prediction in low‐dimensional data with few events

Pavlou, M., Ambler, G., Seaman, S. R., Iorio, M. D., & Omar, R. (2015) · Statistics in Medicine · doi:10.1002/sim.6782 · Cited by 421 · see citing works

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

Andaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ · doi:10.1136/bmj.n2281 · Cited by 415 · see citing works

The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression

van den Goorbergh, R., van Smeden, M., Timmerman, D., & CALSTER, B. V. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac093 · Cited by 387 · see citing works

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Liebal, U. W., An, P. N. T., Sudhakar, M., Raman, K., & Blank, L. M. (2020) · Metabolites · doi:10.3390/metabo10060243 · Cited by 371 · see citing works

Machine learning and deep learning applications in microbiome research

Medina, R. H., Kutuzova, S., Nielsen, K. N., Johansen, J., Hansen, L. H., Nielsen, M. E., & Rasmussen, S. (2022) · ISME Communications · doi:10.1038/s43705-022-00182-9 · Cited by 356 · see citing works

Predictive modeling of biomass gasification with machine learning-based regression methods

Elmaz, F., Yücel, Ö., & Mutlu, A. Y. (2019) · Energy · doi:10.1016/j.energy.2019.116541 · Cited by 282 · see citing works

Comparison of variable selection methods for clinical predictive modeling

Sanchez-Pinto, L. N., Venable, L. R., Fahrenbach, J. P., & Churpek, M. M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.05.006 · Cited by 281 · see citing works

Using Resistin, glucose, age and BMI to predict the presence of breast cancer

Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P. N., Gomes, M., Seiça, R. M. F., & Caramelo, F. J. (2018) · BMC Cancer · doi:10.1186/s12885-017-3877-1 · Cited by 277 · see citing works

Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards

da Costa, C. A., Pasluosta, C. F., Eskofier, B. M., da Silva, D. B., & Righi, R. D. R. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.05.005 · Cited by 278 · see citing works

Statistical Primer: developing and validating a risk prediction model†

Grant, S. W., Collins, P. G. S., & Nashef, S. A. M. (2018) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezy180 · Cited by 254 · see citing works

Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models

Bailly, A., Blanc, C., Francis, É., Guillotin, T., Jamal, F., Wakim, B., & Roy, P. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106504 · Cited by 252 · see citing works

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

Shillan, D., Sterne, J. A. C., Champneys, A., & Gibbison, B. (2019) · Critical Care · doi:10.1186/s13054-019-2564-9 · Cited by 251 · see citing works

Machine Learning in Epidemiology and Health Outcomes Research

Wiemken, T. L., & Kelley, R. R. (2019) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040119-094437 · Cited by 228 · see citing works

Multivariable Modeling Strategies

Harrell, F. E. (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7_4 · Cited by 221 · see citing works

Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges

Meehan, A. J., Lewis, S. J., Fazel, S., Fusar-Poli, P., Steyerberg, E. W., Stahl, D., & Danese, A. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01528-4 · Cited by 219 · see citing works

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Gravesteijn, B. Y., Nieboer, D., Ercole, A., Lingsma, H. F., Nelson, D., CALSTER, B. V., Steyerberg, E. W., Åkerlund, C. A. I., Amrein, K., Anđelić, N., et al. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.03.005 · Cited by 184 · see citing works

Poor performance of clinical prediction models: the harm of commonly applied methods

Steyerberg, E. W., Uno, H., Ioannidis, J. P. A., CALSTER, B. V., Ukaegbu, C. I., Dhingra, T. G., Syngal, S., & Kastrinos, F. (2017) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2017.11.013 · Cited by 166 · see citing works

Communication and computation efficiency in Federated Learning: A survey

Almanifi, O. R. A., Chow, C., Tham, M.-L., Chuah, J. H., & Kanesan, J. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100742 · Cited by 169 · see citing works

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data

Richter, A. N., & Khoshgoftaar, T. M. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.06.002 · Cited by 162 · see citing works

Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts

Gibson, P. B., Chapman, W. E., Altınok, A., Monache, L. D., DeFlorio, M. J., & Waliser, D. E. (2021) · Communications Earth & Environment · doi:10.1038/s43247-021-00225-4 · Cited by 165 · see citing works

Introducing stacking machine learning approaches for the prediction of rock deformation

Koopialipoor, M., Asteris, P. G., Mohammed, A. S., Alexakis, D. Ε., Mamou, A., & Armaghani, D. J. (2022) · Transportation Geotechnics · doi:10.1016/j.trgeo.2022.100756 · Cited by 160 · see citing works

Machine Learning and Psychological Research: The Unexplored Effect of Measurement

Jacobucci, R., & Grimm, K. J. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620902467 · Cited by 160 · see citing works

General Aspects of Fitting Regression Models

Harrell, F. E. (2015) · Springer series in statistics · doi:10.1007/978-3-319-19425-7_2 · Cited by 153 · see citing works

APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support

Kwong, J. C. C., Khondker, A., Lajkosz, K., McDermott, M. B. A., Borrat, X., McCradden, M. D., Mamdani, M., Kulkarni, G. S., & Johnson, A. E. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.35377 · Cited by 151 · see citing works

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

Lynam, A. L., Dennis, J. M., Owen, K. R., Oram, R. A., Jones, A. G., Shields, B. M., & Ferrat, L. A. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00075-2 · Cited by 142 · see citing works

Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis

Benedetto, U., Dimagli, A., Sinha, S., Cocomello, L., Gibbison, B., Caputo, M., Gaunt, T. R., Lyon, M., Holmes, C., & Angelini, G. D. (2020) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2020.07.105 · Cited by 142 · see citing works

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

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

Voice patterns in schizophrenia: A systematic review and Bayesian meta-analysis

Parola, A., Simonsen, A., Bliksted, V., & Fusaroli, R. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.11.031 · Cited by 141 · see citing works

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01577-x · Cited by 140 · see citing works

Improving Prognostic Accuracy in Subjects at Clinical High Risk for Psychosis: Systematic Review of Predictive Models and Meta-analytical Sequential Testing Simulation

Schmidt, A., Cappucciati, M., Raduà, J., Rutigliano, G., Rocchetti, M., Dell’Osso, L., Politi, P., Borgwardt, S., Reilly, T. J., Valmaggia, L., et al. (2016) · Schizophrenia Bulletin · doi:10.1093/schbul/sbw098 · Cited by 131 · see citing works

Machine Learning in Pharmacometrics: Opportunities and Challenges

McComb, M., Bies, R., & Ramanathan, M. (2022) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14801 · Cited by 128 · see citing works

Are Female Soccer Players at an Increased Risk of Second Anterior Cruciate Ligament Injury Compared With Their Athletic Peers?

Allen, M. M., Pareek, A., Krych, A. J., Hewett, T. E., Levy, B. A., Stuart, M. J., & Dahm, D. L. (2016) · The American Journal of Sports Medicine · doi:10.1177/0363546516648439 · Cited by 127 · see citing works

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

Seto, H., Oyama, A., Kitora, S., Toki, H., Yamamoto, R., Kotoku, J., Haga, A., Shinzawa, M., Yamakawa, M., Fukui, S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-20149-z · Cited by 142 · see citing works

Embryo selection with artificial intelligence: how to evaluate and compare methods?

Kragh, M. F., & Karstoft, H. (2021) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-021-02254-6 · Cited by 130 · see citing works

A scoping review of artificial intelligence-based methods for diabetes risk prediction

Mohsen, F., Al-Absi, H. R. H., Yousri, N. A., El Hajj, N., & Shah, Z. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00933-5 · Cited by 125 · see citing works

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Andaur Navarro, C. L., Damen, J. A. A., van Smeden, M., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., et al. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.11.015 · Cited by 121 · see citing works

Systematic misestimation of machine learning performance in neuroimaging studies of depression

Flint, C., Cearns, M., Opel, N., Redlich, R., Mehler, D. M. A., Emden, D., Winter, N. R., Leenings, R., Eickhoff, S. B., Kircher, T., et al. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01020-7 · Cited by 120 · see citing works

Wildfire refugia in forests: Severe fire weather and drought mute the influence of topography and fuel age

Collins, L., Bennett, A. F., Leonard, S., & Penman, T. D. (2019) · Global Change Biology · doi:10.1111/gcb.14735 · Cited by 120 · see citing works

Predicting breast cancer 5-year survival using machine learning: A systematic review

Li, J., Zhou, Z., Dong, J., Fu, Y., Li, Y., Luan, Z., & Peng, X. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250370 · Cited by 123 · see citing works

MACUSTAR: Development and Clinical Validation of Functional, Structural, and Patient-Reported Endpoints in Intermediate Age-Related Macular Degeneration

Finger, R. P., Schmitz-Valckenberg, S., Schmid, M. C., Rubin, G. S., Dunbar, H., Tufail, A., Crabb, D. P., Binns, A. M., Sá‎nchez, C. I., Margaron, P., et al. (2018) · Ophthalmologica · doi:10.1159/000491402 · Cited by 111 · see citing works

Postoperative delirium prediction using machine learning models and preoperative electronic health record data

Bishara, A. M., Chiu, C., Whitlock, E. L., Douglas, V. C., Lee, S., Butte, A. J., Leung, J. M., & Donovan, A. L. (2022) · BMC Anesthesiology · doi:10.1186/s12871-021-01543-y · Cited by 112 · see citing works

Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models

Senanayake, S. J., White, N. M., Graves, N., Healy, H. G., Baboolal, K., & Kularatna, S. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.103957 · Cited by 108 · see citing works

Evaluating the effect of dataset size on predictive model using supervised learning technique

Ajiboye, A. R., Abdullah-Arshah, R., Qin, H., & Isah-Kebbe, H. (2015) · International Journal of Computer Systems & Software Engineering · doi:10.15282/ijsecs.1.2015.6.0006 · Cited by 104 · see citing works

Predicting hypertension using machine learning: Findings from Qatar Biobank Study

AlKaabi, L. A., Ahmed, L. S., Attiyah, M. F. A., & Abdel‐Rahman, M. E. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240370 · Cited by 109 · see citing works

Artificial intelligence-enabled decision support in nephrology

Loftus, T. J., Shickel, B., Ozrazgat‐Baslanti, T., Ren, Y., Glicksberg, B. S., Cao, J., Singh, K., Chan, L., Nadkarni, G. N., & Bihorac, A. (2022) · Nature Reviews Nephrology · doi:10.1038/s41581-022-00562-3 · Cited by 109 · see citing works

Predicting population health with machine learning: a scoping review

Morgenstern, J., Buajitti, E., O’Neill, M., Piggott, T., Goel, V., Fridman, D., Kornas, K., & Rosella, L. C. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-037860 · Cited by 98 · see citing works

Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review

Tan, K. R., Seng, J. J. B., Kwan, Y. H., Chen, Y. J., Zainudin, S. B., Loh, D. H. F., Liu, N., & Low, L. L. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211056917 · Cited by 103 · see citing works

An introduction to machine learning for classification and prediction

Black, J. E., Kueper, J. K., & Williamson, T. S. (2022) · Family Practice · doi:10.1093/fampra/cmac104 · Cited by 103 · see citing works

A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare

Kosolwattana, T., Liu, C., Hu, R., Han, S., Chen, H., & Lin, Y. (2023) · BioData Mining · doi:10.1186/s13040-023-00330-4 · Cited by 106 · see citing works

Prognostic models will be victims of their own success, unless…

Lenert, M. C., Matheny, M. E., & Walsh, C. G. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz145 · Cited by 90 · see citing works

Prediction of transition to psychosis in patients with a clinical high risk for psychosis: a systematic review of methodology and reporting

Studerus, E., Ramyead, A., & Riecher‐Rössler, A. (2017) · Psychological Medicine · doi:10.1017/s0033291716003494 · Cited by 91 · see citing works

Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia

Sufriyana, H., Wu, Y., & Su, E. C. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.102710 · Cited by 100 · see citing works

Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study

Clift, A. K., Dodwell, D., Lord, S., Petrou, S., Brady, M., Collins, G. S., & Hippisley-Cox, J. (2023) · BMJ · doi:10.1136/bmj-2022-073800 · Cited by 97 · 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

Importance of sample size on the quality and utility of AI-based prediction models for healthcare

Riley, R. D., Ensor, J., Snell, K. I. E., Archer, L., Whittle, R., Dhiman, P., Alderman, J. E., Liu, X., Kirton, L., Manson-Whitton, J., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.01.013 · Cited by 93 · see citing works

The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review

Kusuma, K., Larsen, M. E., Quiroz, J. C., Gillies, M. B., Burnett, A., Qian, J., & Torok, M. H. (2022) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2022.09.050 · Cited by 90 · see citing works

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Arina, P., Kaczorek, M. R., Hofmaenner, D. A., Pisciotta, W., Refinetti, P., Singer, M., Mazomenos, E. B., & Whittle, J. (2024) · Anesthesiology · doi:10.1097/aln.0000000000004764 · Cited by 81 · see citing works

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Hoogland, J., IntHout, J., Belias, M., Rovers, M. M., Riley, R. D., Harrell, F. E., Moons, K. G. M., Debray, T. P. A., & Reitsma, J. B. (2021) · Statistics in Medicine · doi:10.1002/sim.9154 · Cited by 79 · see citing works

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology

Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2022) · Diagnostic and Prognostic Research · doi:10.1186/s41512-022-00126-w · Cited by 79 · see citing works

Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis

Garcés, P., Baumeister, S., Mason, L., Chatham, C. H., Holiga, Š., Dukart, J., Jones, E. J. H., Banaschewski, T., Baron‐Cohen, S., Bölte, S., et al. (2022) · Molecular Autism · doi:10.1186/s13229-022-00500-x · Cited by 78 · see citing works

Just How Confident Can We Be in Predicting Sports Injuries? A Systematic Review of the Methodological Conduct and Performance of Existing Musculoskeletal Injury Prediction Models in Sport

Bullock, G. S., Mylott, J. A., Hughes, T., Nicholson, K. F., Riley, R. D., & Collins, P. G. S. (2022) · Sports Medicine · doi:10.1007/s40279-022-01698-9 · Cited by 88 · see citing works

Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis

Tang, R., Luo, R., Tang, S., Song, H., & Chen, X. (2022) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2022.106684 · Cited by 82 · see citing works

Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the “large N, small p” setting

Austin, P. C., Harrell, F. E., & Steyerberg, E. W. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211002867 · Cited by 74 · see citing works

Predicting relapse or recurrence of depression: systematic review of prognostic models

Moriarty, A. S., Meader, N., Snell, K. I. E., Riley, R. D., Paton, L. W., Dawson, S., Hendon, J., Chew-Graham, C. A., Gilbody, S., Churchill, R., et al. (2022) · The British Journal of Psychiatry · doi:10.1192/bjp.2021.218 · Cited by 71 · see citing works

Early PREdiction of sepsis using leukocyte surface biomarkers: the ExPRES-sepsis cohort study

Shankar-Hari, M., Datta, D., Wilson, J., Assi, V., Stephen, J., Weir, C. J., Rennie, J., Antonelli, J., Bateman, A., Felton, J. M., et al. (2018) · Intensive Care Medicine · doi:10.1007/s00134-018-5389-0 · Cited by 74 · see citing works

Return to Work After Traumatic Injury: Increased Work-Related Disability in Injured Persons Receiving Financial Compensation is Mediated by Perceived Injustice

Giummarra, M. J., Cameron, P. A., Ponsford, J., Ioannou, L., Gibson, S. J., Jennings, P. A., & Georgiou-Karistianis, N. (2017) · Journal of Occupational Rehabilitation · doi:10.1007/s10926-016-9642-5 · Cited by 68 · see citing works

Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

Speiser, J. L., Callahan, K. E., Houston, D. K., Fanning, J. T., Gill, T. M., Guralnik, J. M., Newman, A. B., Pahor, M., Rejeski, W. J., & Miller, M. E. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa138 · Cited by 68 · see citing works

Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data

Wang, X., Ren, H., Ren, J., Song, W., Qiao, Y., Ren, Z., Zhao, Y., Linghu, L., Cui, Y., Zhao, Z., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107340 · Cited by 66 · see citing works

Strength of Association between Pentosan Polysulfate and a Novel Maculopathy

Hanif, A. M., Shah, R., Yan, J., Varghese, J. S., Patel, S. A., Cribbs, B. E., O’Keefe, G. A. D., Hendrick, A. M., Shantha, J. G., Hubbard, G. B., et al. (2019) · Ophthalmology · doi:10.1016/j.ophtha.2019.04.024 · Cited by 64 · see citing works

Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals

Sánchez‐Cabo, F., Rosselló, X., Fuster, V., Benito, F., Manzano, J. P., Silla-Castro, J. C., Fernández-Alvira, J. M., Oliva, B., Fernández‐Friera, L., López‐Melgar, B., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.08.017 · Cited by 65 · see citing works

Big Data in Head and Neck Cancer

Resteghini, C., Trama, A., Borgonovi, E., Hosni, H., Corrao, G., Orlandi, E. M., Calareso, G., Cecco, L. D., Piazza, C., Mainardi, L. T., et al. (2018) · Current Treatment Options in Oncology · doi:10.1007/s11864-018-0585-2 · Cited by 62 · see citing works

Predicting high confidence errors in eyewitness memory: The role of face recognition ability, decision-time, and justifications.

Grabman, J. H., Dobolyi, D. G., Berelovich, N. L., & Dodson, C. S. (2019) · Journal of Applied Research in Memory and Cognition · doi:10.1037/h0101835 · Cited by 64 · see citing works

Applied Machine Learning for Spine Surgeons: Predicting Outcome for Patients Undergoing Treatment for Lumbar Disc Herniation Using PRO Data

Pedersen, C. F., Andersen, M. Ø., Carreon, L. Y., & Eiskjær, S. P. (2020) · Global Spine Journal · doi:10.1177/2192568220967643 · Cited by 63 · see citing works

Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review

Lai, Q., Spoletini, G., Mennini, G., Laureiro, Z. L., Tsilimigras, D. I., Pawlik, T. M., & Rossi, M. (2020) · World Journal of Gastroenterology · doi:10.3748/wjg.v26.i42.6679 · Cited by 63 · see citing works

Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

Gazis, I.-Z., Schoening, T., Alevizos, E., & Greinert, J. (2018) · Biogeosciences · doi:10.5194/bg-15-7347-2018 · Cited by 62 · see citing works

Fold-stratified cross-validation for unbiased and privacy-preserving federated learning

Bey, R., Goussault, R., Grolleau, F., Benchoufi, M., & Porcher, R. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa096 · Cited by 60 · see citing works

Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis

Xiong, S., Chen, W., Jia, X., Jia, Y., & Liu, C. (2023) · BMC Pulmonary Medicine · doi:10.1186/s12890-023-02570-w · Cited by 60 · see citing works

Modern modeling techniques had limited external validity in predicting mortality from traumatic brain injury

van der Ploeg, T., Nieboer, D., & Steyerberg, E. W. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.03.002 · Cited by 59 · see citing works

Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China

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

Sample Size Requirements for Popular Classification Algorithms in Tabular Clinical Data: Empirical Study

Silvey, S. G., & Liu, J. (2024) · Journal of Medical Internet Research · doi:10.2196/60231 · Cited by 59 · see citing works

Evidence of Inflated Prediction Performance: A Commentary on Machine Learning and Suicide Research

Jacobucci, R., Littlefield, A. K., Millner, A. J., Kleiman, E. M., & Steinley, D. (2021) · Clinical Psychological Science · doi:10.1177/2167702620954216 · Cited by 56 · see citing works

Identifying predictive factors for neuropathic pain after breast cancer surgery using machine learning

Juwara, L., Arora, N., Gornitsky, M., Saha‐Chaudhuri, P., & Velly, A. M. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104170 · Cited by 56 · see citing works

Not just “big” data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions

McNamara, M. E., Zisser, M., Beevers, C. G., & Shumake, J. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2022.104086 · Cited by 56 · see citing works

Ethnic disparities in children’s oral health: findings from a population-based survey of grade 1 and 2 schoolchildren in Alberta, Canada

Shi, C., Faris, P., McNeil, D. A., Patterson, S. K., Potestio, M. L., Thawer, S., & McLaren, L. (2018) · BMC Oral Health · doi:10.1186/s12903-017-0444-8 · Cited by 56 · see citing works

Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer

Strijker, M., Chen, J., Mungroop, T. H., Jamieson, N. B., van Eijck, C. H. J., Steyerberg, E. W., Wilmink, J. W., Koerkamp, B. G., van Laarhoven, H. W. M., & Besselink, M. G. (2019) · British journal of surgery · doi:10.1002/bjs.11111 · Cited by 55 · see citing works

Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty

Pua, Y., Kang, H., Thumboo, J., Clark, R., Chew, E., Poon, C. L.-L., Chong, H., & Yeo, S. (2019) · Knee Surgery, Sports Traumatology, Arthroscopy · doi:10.1007/s00167-019-05822-7 · Cited by 49 · see citing works

Interpretable machine learning for materials design

Dean, J., Scheffler, M., Purcell, T. A. R., Barabash, S. V., Bhowmik, R., & Bazhirov, T. (2023) · Journal of materials research/Pratt's guide to venture capital sources · doi:10.1557/s43578-023-01164-w · Cited by 54 · see citing works

Predicting lung cancer survival based on clinical data using machine learning: A review

Altuhaifa, F. A., Win, K. T., & Su, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107338 · Cited by 53 · see citing works

A Novel Risk Calculator Predicts 90-Day Readmission Following Total Joint Arthroplasty

Goltz, D. E., Ryan, S. P., Hopkins, T. J., Howell, C. B., Attarian, D. E., Bolognesi, M. P., & Seyler, T. M. (2019) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.18.00843 · Cited by 52 · see citing works

Feasibility of Machine Learning and Logistic Regression Algorithms to Predict Outcome in Orthopaedic Trauma Surgery

Oosterhoff, J. H. F., Gravesteijn, B. Y., Karhade, A. V., Jaarsma, R. L., Kerkhoffs, G. M. M. J., Ring, D., Schwab, J. H., Steyerberg, E. W., & Doornberg, J. N. (2021) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.21.00341 · Cited by 52 · see citing works

Classification by ordinal sums of conjunctive and disjunctive functions for explainable AI and interpretable machine learning solutions

Hudec, M., Mináriková, E., Mesiar, R., Saranti, A., & Holzinger, A. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106916 · Cited by 51 · see citing works

Prediction of Medical Outcomes with Modern Modelling Techniques

van der Ploeg, T. (2017) · EUR Research Repository (Erasmus University Rotterdam) · Cited by 1 · see citing works

A Comparison of Different Modeling Techniques in Predicting Mortality With the Tilburg Frailty Indicator: Longitudinal Study

van der Ploeg, T., & Gobbens, R. J. J. (2022) · JMIR Medical Informatics · doi:10.2196/31480 · Cited by 9 · see citing works

The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration

Austin, P. C., Lee, D. S., & Wang, B. (2024) · Diagnostic and Prognostic Research · doi:10.1186/s41512-024-00179-z · Cited by 11 · see citing works

Predictive performance of multinomial logistic prediction models– a simulation study

de Jong, V. M. T. (2016)

A Simulation Study to Compare the Predictive Performance of Survival Neural Networks with Cox Models for Clinical Trial Data

Kantidakis, G., Biganzoli, E. M., Putter, H., & Fiocco, M. F. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/2160322 · Cited by 15 · see citing works