Random survival forests

Random Forests

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

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

Classification and Regression by randomForest

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

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001

Mokdad, A. H., Ford, E. S., Bowman, B. A., Dietz, W. H., Vinicor, F., Bales, V. S., & Marks, J. S. (2003) · JAMA · doi:10.1001/jama.289.1.76 · Cited by 5,866

Excess Deaths Associated With Underweight, Overweight, and Obesity

Flegal, K. M. (2005) · JAMA · doi:10.1001/jama.293.15.1861 · Cited by 2,811

A Potential Decline in Life Expectancy in the United States in the 21st Century

Olshansky, S. J., Passaro, D. J., Hershow, R. C., Layden, J., Carnes, B. A., Brody, J., Hayflick, L., Butler, R. N., Allison, D. B., & Ludwig, D. S. (2005) · New England Journal of Medicine · doi:10.1056/nejmsr043743 · Cited by 2,612

Boosting the margin: a new explanation for the effectiveness of voting methods

Bartlett, P. L., Freund, Y., Lee, W. S., & Schapire, R. E. (1998) · The Annals of Statistics · doi:10.1214/aos/1024691352 · Cited by 2,574

Years of Life Lost Due to Obesity

Fontaine, K. R., Redden, D. T., Wang, C., Westfall, A. O., & Allison, D. B. (2003) · JAMA · doi:10.1001/jama.289.2.187 · Cited by 2,551

Overweight, Obesity, and Mortality in a Large Prospective Cohort of Persons 50 to 71 Years Old

Adams, K. F., Schatzkin, A. G., Harris, T. B., Kipnis, V., Mouw, T., Ballard-Barbash, R. R., Hollenbeck, A. R., & Leitzmann, M. F. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa055643 · Cited by 2,147

A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction

Montesinos-López, O. A., Mosqueda-González, B. A., González, A. P., Montesinos‐López, A., & Crossa, J. L. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.887643 · Cited by 15

Predicting foraging dive outcomes in chinstrap penguins using biologging and animal-borne cameras

Manco, F., Lang, S. D. J., & Trathan, P. N. (2022) · Behavioral Ecology · doi:10.1093/beheco/arac066 · Cited by 14

Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures

Sonabend, R., Bender, A., & Vollmer, S. J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac451 · Cited by 15

Deep survival modeling of longitudinal retinal OCT volumes for predicting the onset of atrophy in patients with intermediate AMD

Rivail, A., Vogl, W., Riedl, S., Grechenig, C., Coulibaly, L. M., Reiter, G. S., Guymer, R. H., Wu, Z., Schmidt-Erfurth, U. M., & Bogunović, H. (2023) · Biomedical Optics Express · doi:10.1364/boe.487206 · Cited by 15

Latent Coronary Plaque Morphology From Computed Tomography Angiography, Molecular Disease Activity on Positron Emission Tomography, and Clinical Outcomes

Kwieciński, J., Kolossváry, M., Tzolos, E., Meah, M. N., Adamson, P. D., Joshi, N. V., Williams, M. C., van Beek, E. J. R., Berman, D. S., Maurovich‐Horvat, P., et al. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.123.319332 · Cited by 15

Interpretable Machine Learning for Choosing Radiation Dose-volume Constraints on Cardio-pulmonary Substructures Associated with Overall Survival in NRG Oncology RTOG 0617

Lee, S. H., Geng, H., Arnold, J., Caruana, R. A., Yong Fan, A., Rosen, M. A., Apte, A., Deasy, J. O., Bradley, J. D., & Xiao, Y. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.06.009 · Cited by 15

ColoFinder: a prognostic 9-genes signature improve prognosis for 871 stage II and III colorectal cancer patients

Shi, M., & He, J. (2016) · PeerJ · doi:10.7717/peerj.1804 · Cited by 15

Random rotation survival forest for high dimensional censored data

Zhou, L., Wang, H., & Xu, Q. (2016) · SpringerPlus · doi:10.1186/s40064-016-3113-5 · Cited by 15

Topologically inferring pathway activity for precise survival outcome prediction: breast cancer as a case

Liu, W., Wang, W., Tian, G., Xie, W., Li, L., Liu, J., Huang, W., Xu, L., & Li, E. (2017) · Molecular BioSystems · doi:10.1039/c6mb00757k · Cited by 15

Treatment-Free Survival in Patients With Differentiated Thyroid Cancer

Banerjee, M., Reyes‐Gastelum, D., & Haymart, M. R. (2018) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-00511 · Cited by 15

Urgent surgery in colon cancer has no impact on survival

Antony, P., Harnoss, J. C., Warschkow, R., Schmied, B. M., Schneider, M., Tarantino, I., & Ulrich, A. B. (2019) · Journal of Surgical Oncology · doi:10.1002/jso.25469 · Cited by 15

Predictive Analysis of First Abbreviated New Drug Application Submission for New Chemical Entities Based on Machine Learning Methodology

Hu, M., Babiskin, A. H., Wittayanukorn, S., Schick, A., Rosenberg, M., Gong, X., Kim, M., Zhang, L., Lionberger, R. A., & Zhao, L. Y. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1479 · Cited by 15

The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non–Small Cell Lung Cancer

He, J., Zhang, J., Chen, C.-T., Ma, Y., Guzman, R. D., Meng, J., & Pu, Y. (2020) · Medical Care · doi:10.1097/mlr.0000000000001288 · Cited by 15

What is the truth behind pump thrombosis in the HeartMate II device? A National Heart, Lung, and Blood Institute perspective based on data from the Interagency Registry for Mechanically Assisted Circulatory Support

Jeffries, N. O., Miller, M. A., Taddei‐Peters, W. C., Burke, C., Baldwin, J. T., & Young, J. B. (2015) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2015.10.036 · Cited by 15

MMOSurv: meta-learning for few-shot survival analysis with multi-omics data

Wen, G., & Li, L. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae684 · Cited by 14

A Review of Tree‐Based Methods for Analyzing Survey Data

Bhaduri, D., Toth, D., & Holan, S. H. (2025) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.70010 · Cited by 14

Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer

Bai, R., Li, J.-W., Hong, X., Xuan, X.-Y., Li, X., & Tuo, Y. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-025-07433-2 · Cited by 14

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases

Lindén, T., Hanses, F., Domingo‐Fernándéz, D., DeLong, L. N., Kodamullil, A. T., Schneider, J., Vehreschild, M. J. G. T., Lanznaster, J., Ruethrich, M. M., Borgmann, S., et al. (2021) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2021.100020 · Cited by 17

Personalized Antiviral Drug Selection in Patients With Chronic Hepatitis B Using a Machine Learning Model: A Multinational Study

Hur, M. H., Park, M. K., Yip, T. C., Chen, C., Lee, H., Choi, W., Kim, S. U., Lim, Y., Park, S. Y., Wong, G. L., et al. (2023) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000002234 · Cited by 14

Survival analysis of breast cancer patients using machine learning models

Evangeline, K. C., Kirubha, S. P. A., & Precious, J. G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14989-8 · Cited by 15

Identification of representative trees in random forests based on a new tree-based distance measure

Laabs, B., Westenberger, A., & König, I. R. (2023) · Advances in Data Analysis and Classification · doi:10.1007/s11634-023-00537-7 · Cited by 14

Syndromic Gastrointestinal Panel Diagnostic Tests Have Changed our Understanding of the Epidemiology of Yersiniosis —Foodborne Diseases Active Surveillance Network, 2010-2021

Ray, L. C., Payne, D. C., Rounds, J. M., Trevejo, R. T., Wilson, E. L., Burzlaff, K. E., Garman, K. N., Lathrop, S. L., Rissman, T., Wymore, K., et al. (2024) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofae199 · Cited by 14

Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure

Truchot, A., Raynaud, M., Helanterä, I., Aubert, O., Kamar, N., Divard, G., Astor, B. C., Legendre, C. M., Hertig, A., Büchler, M., et al. (2024) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000517 · Cited by 14

Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery

Yan, H., Ji, X., & Li, B. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1572034 · Cited by 14

Confidence scores for prediction models

Gerds, T. A., & van de Wiel, M. A. (2011) · Biometrical Journal · doi:10.1002/bimj.201000157 · Cited by 14

A survival ensemble of extreme learning machine

Wang, H., Wang, J., & Zhou, L. (2017) · Applied Intelligence · doi:10.1007/s10489-017-1063-4 · Cited by 14

Efficient document image binarization using heterogeneous computing and parameter tuning

Westphal, F., Grahn, H., & Lavesson, N. (2018) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-017-0293-7 · Cited by 14

An Inverse Optimization Approach to Measuring Clinical Pathway Concordance

Chan, T. C. Y., Eberg, M., Forster, K., Holloway, C. M. B., Ieraci, L., Shalaby, Y., & Yousefi, N. (2021) · Management Science · doi:10.1287/mnsc.2021.4100 · Cited by 17

HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures

Li, M., Zhao, J., Li, X., Chen, Y., Feng, C., Qian, F., Liu, Y., Zhang, J., He, J., Ai, B., et al. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz078 · Cited by 14

The role of elective neck dissection in high‐grade parotid malignancy: A hospital‐based cohort study

Harbison, R. A., Gray, A. J., Westling, T., Carone, M., Rodriguez, C. P., Futran, N. D., Cannon, R. B., & Houlton, J. J. (2019) · The Laryngoscope · doi:10.1002/lary.28238 · Cited by 14

Integration of Cancer Genomics Data for Tree‐based Dimensionality Reduction and Cancer Outcome Prediction

Shi, M., Wang, J., & Zhang, C. (2019) · Molecular Informatics · doi:10.1002/minf.201900028 · Cited by 14

Avoiding stranded bicycles in free-floating bicycle-sharing systems: using survival analysis to derive operational rules for rebalancing

Guidon, S., Becker, H., & Axhausen, K. W. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8916869 · Cited by 14

Pseudogene-gene functional networks are prognostic of patient survival in breast cancer

Smerekanych, S., Johnson, T. S., Huang, K., & Zhang, Y. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0687-0 · Cited by 14

Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features

Patel, H. J., Vock, D. M., Marai, G. E., Fuller, C. D., Mohamed, A., & Canahuate, G. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-92072-8 · Cited by 14

SURVFIT: Doubly sparse rule learning for survival data

Shakur, A. H., Huang, S., Qian, X., & Chang, X. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103691 · Cited by 13

Association of atrial septal fenestration with outcomes after atrioventricular septal defect repair

Callahan, C. P., Jegatheeswaran, A., Barron, D. J., Husain, S. A., Fuller, S., Overman, D. M., McCrindle, B. W., Blackstone, E. H., Caldarone, C. A., Dabal, R. J., et al. (2021) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2021.06.067 · Cited by 14

Machine Learning Incorporating Host Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients

Yu, H., Ma, S. J., Farrugia, M. K., Iovoli, A. J., Wooten, K. E., Gupta, V., McSpadden, R. P., Kuriakose, M. A., Markiewicz, M. R., Chan, J. M., et al. (2021) · Cancers · doi:10.3390/cancers13184559 · Cited by 14

Machine learning identifies the independent role of dysplasia in the prediction of response to chemotherapy in AML

Duchmann, M., Wagner‐Ballon, O., Boyer, T., Cheok, M. H.-C., Fournier, É., Guérin, E., Fenwarth, L., Badaoui, B., Freynet, N., Benayoun, E., et al. (2021) · Leukemia · doi:10.1038/s41375-021-01435-7 · Cited by 15

Improving the predictability and interpretability of co‐occurrence modelling through feature‐based joint species distribution ensembles

Powell‐Romero, F., Fountain‐Jones, N. M., Norberg, A., & Clark, N. J. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13915 · Cited by 14

Risk Prediction of Pancreatic Cancer in Patients With Abnormal Morphologic Findings Related to Chronic Pancreatitis: A Machine Learning Approach

Chen, W., Chen, Q., Parker, R. A., Zhou, Y., Lustigova, E., & Wu, B. U. (2022) · Gastro Hep Advances · doi:10.1016/j.gastha.2022.06.008 · Cited by 14

Data-driven integrated care pathways: Standardization of delivering patient-centered care

Han, S., & Ma, L. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.883874 · Cited by 15

Nonparametric Failure Time: Time-to-Event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees and Low Information Omnibus Dirichlet Process Mixtures

Sparapani, R., Logan, B. R., Maiers, M. J., Laud, P. W., & McCulloch, R. E. (2023) · Biometrics · doi:10.1111/biom.13857 · Cited by 14

Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis

Dayao, M. T., Trevino, A. E., Kim, H., Ruffalo, M., D’Angio, H. B., Preska, R., Duvvuri, U., Mayer, A. T., & Bar‐Joseph, Z. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad245 · Cited by 15

Machine learning as a new paradigm for characterizing localization and lateralization of neuropsychological test data in temporal lobe epilepsy

Frank, B. E., Hurley, L., Scott, T. M., Olsen, P., Dugan, P. C., & Barr, W. (2018) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2018.07.006 · Cited by 15

The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition

Guitart, A., Chen, P. P., & Periáñez, Á. (2018) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010016 · Cited by 15

Prognostic risk model construction and molecular marker identification in glioblastoma multiforme based on mRNA/microRNA/long non-coding RNA analysis using random survival forest method

Wang, H., Liu, D., & Yang, J. (2019) · Neoplasma · doi:10.4149/neo_2018_181008n746 · Cited by 14

Time-Dependent Survival Neural Network for Remaining Useful Life Prediction

Zhang, J., Wang, S., Chen, L., Guo, G., Chen, R., & Vanasse, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16148-4_34 · Cited by 14

Analysis of Large Heterogeneous Repairable System Reliability Data With Static System Attributes and Dynamic Sensor Measurement in Big Data Environment

Liu, X., & Pan, R. (2019) · Technometrics · doi:10.1080/00401706.2019.1609584 · Cited by 14

A Decision-Theoretic Approach to Panel-Based, Preemptive Genotyping

Shi, Y., Graves, J. A., Garbett, S. P., Zhou, Z., Marathi, R., Wang, X., Harrell, F. E., Lasko, T. A., Denny, J. C., Roden, D. M., et al. (2019) · MDM Policy & Practice · doi:10.1177/2381468319864337 · Cited by 14

Multihorizon discrete time survival models

Breeden, J. L., & Crook, J. N. (2020) · Journal of the Operational Research Society · doi:10.1080/01605682.2020.1777907 · Cited by 14

Bayesian Weibull tree models for survival analysis of clinico-genomic data

Clarke, J., & West, M. (2007) · Statistical Methodology · doi:10.1016/j.stamet.2007.09.003 · Cited by 14

Genetic population structure of the round whitefish (Prosopium cylindraceum) in North America: multiple markers reveal glacial refugia and regional subdivision

Morgan, T. D., Graham, C. F., McArthur, A. G., Raphenya, A. R., Boreham, D. R., Manzon, R. G., Wilson, J. Y., Lance, S. L., Howland, K. E., Patrick, P. H., et al. (2017) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/cjfas-2016-0528 · Cited by 14

Validation of DNA Methylation to Predict Outcome in Acute Myeloid Leukemia by Use of xMELP

Wertheim, G. B. W., Smith, C. D. M., Luskin, M. R., Rager, A., Figueroa, M. E., Carroll, M. P., & Master, S. R. (2014) · Clinical Chemistry · doi:10.1373/clinchem.2014.229781 · Cited by 13

Repair, Reconstruct, or Divert

Tang, A., Ahmad, U., Raja, S., Siddiqui, H. U., Sinopoli, J. N., O’Dell, A., Pande, A., Blackstone, E. H., & Murthy, S. C. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000003648 · Cited by 13

Comparison of Random Survival Forest with Accelerated Failure Time-Weibull Model for Bridge Deck Deterioration

Lu, M., & Guler, S. I. (2022) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981221078281 · Cited by 13

Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features

Carlini, G., Curti, N., Strolin, S., Giampieri, E., Sala, C., Dall’Olio, D., Merlotti, A., Fanti, S., Remondini, D., Nanni, C., et al. (2022) · Applied Sciences · doi:10.3390/app12125946 · Cited by 13

Predicting Long-term Survival and Time-to-recurrence After Esophagectomy in Patients With Esophageal Cancer

Gujjuri, R. R., Clarke, J., Elliott, J. A., Rahman, S., Reynolds, J. V., Hanna, G. B., Markar, S. R., & ENSURE Study Group (2022) · Annals of Surgery · doi:10.1097/sla.0000000000005538 · Cited by 13

Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside

Chapiro, J., Allen, B., Abajian, A. C., Wood, B. J., Kothary, N., Daye, D., Bai, H. X., Sedrakyan, A., Diamond, M., Simonyan, V. V., et al. (2022) · Journal of Vascular and Interventional Radiology · doi:10.1016/j.jvir.2022.06.003 · Cited by 14

Survival Analysis of High-Dimensional Data With Graph Convolutional Networks and Geometric Graphs

Ling, Y., Liu, Z., & Xue, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3190321 · Cited by 13

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Volpe, S., Isaksson, L. J., Zaffaroni, M., Pepa, M., Raimondi, S., Botta, F., Presti, G. L., Vincini, M. G., Rampinelli, C., Cremonesi, M., et al. (2022) · Translational Lung Cancer Research · doi:10.21037/tlcr-22-248 · Cited by 13

Development of an Electronic Frailty Index for Predicting Mortality in Patients Undergoing Transcatheter Aortic Valve Replacement using Machine Learning

Chen, Y., Zhou, J., Chan, J. S. K., Liu, T., Hothi, S. S., Roever, L., Rajan, R., Wong, I. C. K., Zhang, Q., Tse, G., et al. (2022) · Annals of Clinical Cardiology · doi:10.4103/accj.accj_13_22 · Cited by 13

Fairness with censorship and group constraints

Zhang, W., & Weiss, J. C. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01842-5 · Cited by 13

A2BB’X6/ABX3-type High-performance perovskites screening based on ensemble learning and high throughput screening

Xu, F., Chen, Z., Ma, Q., Wu, J., Lin, J. H., Li, J., Li, W., Liu, C., Shen, H., & You, L. (2023) · Solar Energy · doi:10.1016/j.solener.2023.111795 · Cited by 13

Development of an adaptive clinical web-based prediction tool for kidney replacement therapy in children with chronic kidney disease

Ng, D. K., Matheson, M. B., Schwartz, G. J., Wang, F. M., Mendley, S. R., Furth, S. L., Warady, B. A., Fathallah-Shaykh, S. A., Nayak, A. B., Turman, M. A., et al. (2023) · Kidney International · doi:10.1016/j.kint.2023.06.020 · Cited by 13

DUX4 is a common driver of immune evasion and immunotherapy failure in metastatic cancers

Pineda, J. M. B., & Bradley, R. K. (2023) · eLife · doi:10.7554/elife.89017 · Cited by 15

Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning

Höhn, J., Krieghoff‐Henning, E., Wies, C., Kiehl, L., Hetz, M. J., Bucher, T.-C., Jonnagaddala, J., Zatloukal, K., Müller, H., Plass, M., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00451-3 · Cited by 13

Bayesian parametric models for survival prediction in medical applications

Paolucci, I., Lin, Y., Silva, J. A. M., Brock, K. K., & Odisio, B. C. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02059-4 · Cited by 13

Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data

Zou, Y., Yue, M., Jia, L., Wang, Y., Chen, H., Zhang, A., Xia, X., Liu, W., Yu, R., Yang, S., et al. (2023) · BMC Cancer · doi:10.1186/s12885-023-11628-1 · Cited by 13

Risk factors affecting patients survival with colorectal cancer in Morocco: survival analysis using an interpretable machine learning approach

Badisy, I. E., Brahim, Z. B., Khalis, M., Elansari, S., ElHitmi, Y., Abbass, F., Mellas, N., & Rhazi, K. E. (2024) · Scientific Reports · doi:10.1038/s41598-024-51304-3 · Cited by 13

A review on statistical and machine learning competing risks methods

Monterrubio‐Gómez, K., Constantine‐Cooke, N., & Vallejos, C. A. (2024) · Biometrical Journal · doi:10.1002/bimj.202300060 · Cited by 13

Random forests for survival data: which methods work best and under what conditions?

Berkowitz, M., Altman, R. M., & Loughin, T. M. (2024) · The International Journal of Biostatistics · doi:10.1515/ijb-2023-0056 · Cited by 13

Preoperatively predicting survival outcome for clinical stage IA pure-solid non–small cell lung cancer by radiomics-based machine learning

Yan, H., Niimi, T., Matsunaga, T., Fukui, M., Hattori, A., Takamochi, K., & Suzuki, K. (2024) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2024.05.010 · Cited by 14

Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare

Berloco, F., Marvulli, P. M., Suglia, V., Colucci, S., Pagano, G., Palazzo, L., Aliani, M., Castellana, G., Guido, P., D’Addio, G., et al. (2024) · Applied Sciences · doi:10.3390/app14146084 · Cited by 13

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Moharrami, M., Zavareh, P. A., Watson, E. E., Singhal, S., Johnson, A. E. W., Hosni, A., Quiñonez, C., & Glogauer, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307531 · Cited by 15

AI Survival Prediction Modeling: The Importance of Considering Treatments and Changes in Health Status over Time

Adam, N. R., & Wieder, R. (2024) · Cancers · doi:10.3390/cancers16203527 · Cited by 13

Machine Learning-Based Prediction of Death and Hospitalization in Patients With Implantable Cardioverter Defibrillators

Rosman, L., Lampert, R., Wang, K., Gehi, A. K., Dziura, J. D., Salmoirago‐Blotcher, E., Brandt, C., Sears, S. F., & Burg, M. M. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.09.006 · Cited by 13

Offline Feature-Based Pricing Under Censored Demand: A Causal Inference Approach

Tang, J., Qi, Z., Fang, E. X., & Shi, C. (2025) · Manufacturing & Service Operations Management · doi:10.1287/msom.2024.1061 · Cited by 13

Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan

Yang, K., González, D., Woodhead, J. L., Bhargava, P., & Ramanathan, M. K. (2025) · Clinical and Translational Science · doi:10.1111/cts.70272 · Cited by 16

Intradialytic blood pressure pattern recognition based on density peak clustering

Wang, F., Zhou, J., Tian, Y., Wang, Y., Zhang, P., Chen, J., & Li, J. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.05.013 · Cited by 13

Survival forests under test: Impact of the proportional hazards assumption on prognostic and predictive forests for amyotrophic lateral sclerosis survival

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Survival neural networks for time-to-event prediction in longitudinal study

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