Random survival forests

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Items 501–600 of 1,001

1. Artificial Intelligence for Response Evaluation With PET/CT
   Lise Wei, Issam El Naqa · 2020 · Seminars in Nuclear Medicine
2. Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy
   Yong Chen, Fei Yuan, Lingyun Wang, Elsie Li, Zhihan Xu, Michael Wels, Weiwu Yao, Huan Zhang · 2021 · European Journal of Surgical Oncology
3. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
   Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty · 2021 · Journal of Biomedical Informatics
4. Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data
   Mohanad Mohammed, Innocent B. Mboya, Henry Godwell Mwambi, Murtada Khalafallah Elbashir, Bernard Omolo · 2021 · PLoS ONE
5. Stochastic Modelling of Mineral Exploration Targets
   Hassan Talebi, Ute A. Mueller, Luk J. M. Peeters, Alex Otto, Patrice de Caritat, Raimon Tolosana‐Delgado, K. Gerald van den Boogaart · 2022 · Mathematical Geosciences
6. The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models
   Abdallah Alabdallah, Mattias Ohlsson, Sepideh Pashami, Thorsteinn S. Rognvaldsson · 2024 · Artificial Intelligence in Medicine
7. Prediction of Early Breast Cancer Metastasis from DNA Microarray Data Using High-Dimensional Cox Regression Models
   Christophe Zemmour, François Bertucci, Pascal Finetti, Bernard Chétrit, Daniel Birnbaum, Thomas Filleron, Jean‐Marie Boher · 2015 · Cancer Informatics
8. AK‐nearest neighbors survival probability prediction method
   David J. Lowsky, Yichuan Ding, Donald Lee, Charles E. McCulloch, Lainie Friedman Ross, J. Richard Thistlethwaite, Stefanos A. Zenios · 2013 · Statistics in Medicine
9. Prognostic value of health-related quality of life in patients with metastatic pancreatic adenocarcinoma: a random forest methodology
   Momar Diouf, Thomas Filleron, Anne‐Laure Pointet, Anne-Claire Dupont-Gossart, David Malka, Pascal Artru, Mélanie Gauthier, Thierry Lecomte, Thomas Aparicio, Anne Thirot‐Bidault et al. · 2015 · Quality of Life Research
10. Survival analysis as semi-supervised multi-target regression for time-to-employment prediction using oblique predictive clustering trees
    Viktor Andonovikj, Pavle Boškoski, Sašo Džeroski, Biljana Mileva Boshkoska · 2023 · Expert Systems with Applications
11. Survival analysis for lung cancer patients: A comparison of Cox regression and machine learning models
    Sebastian Germer, Christiane Rudolph, Louisa Labohm, Alexander Katalinic, Natalie Bianca Rath, Katharina Rausch, Bernd Holleczek, Heinz Handels A · 2024 · International Journal of Medical Informatics
12. Discrete‐time survival trees
    Imad Bou-Hamad, Denis Larocque, Hatem Ben‐Ameur, Louise C. Mâsse, Frank Vitaro, Richard Ernest Tremblay · 2009 · Canadian Journal of Statistics
13. Comparison of Cox Model Methods in a Low-Dimensional Setting with Few Events
    Francisco Ojeda, Christian Müller, Daniela Börnigen, David‐Alexandre Trégouët, Arne Schillert, Matthias Heinig, Tanja Zeller, Renate B. Schnabel · 2016 · Genomics Proteomics & Bioinformatics
14. The prognostic value of dynamic contrast-enhanced MRI contrast agent transfer constant Ktrans in cervical cancer is explained by plasma flow rather than vessel permeability
    Ben Dickie, Chris J. Rose, Lucy Elizabeth Kershaw, Stephanie Withey, Bernadette M. Carrington, Susan E. Davidson, Gillian Hutchison, Catharine West · 2017 · British Journal of Cancer
15. PPAR and GST polymorphisms may predict changes in intellectual functioning in medulloblastoma survivors
    Adeoye A. Oyefiade, Lauren Erdman, Anna Goldenberg, David M. Malkin, Éric Bouffet, Michael D. Taylor, Vijay Ramaswamy, Nadia Scantlebury, Nicole Law, Donald Mabbott · 2019 · Journal of Neuro-Oncology
16. Estimating customer churn under competing risks
    Pallav Routh, Arkajyoti Roy, Jeffrey Meyer · 2020 · Journal of the Operational Research Society
17. Random Survival Forests Analysis of Intraoperative Complications as Predictors of Descemet Stripping Automated Endothelial Keratoplasty Graft Failure in the Cornea Preservation Time Study.
    Robert C. O’Brien, H. Ishwaran, L. Szczotka-Flynn, J. Lass · 2020 · JAMA ophthalmology
18. A comparison of importance of modelling method and sample size for mapping soil organic matter in Guangdong, China
    Yu-Qing Lai, Hui-Li Wang, Xiaolin Sun · 2021 · Ecological Indicators
19. The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma
    Hajar Moradmand, Seyed Mahmoud Reza Aghamiri, Reza Ghaderi, Hamid Emami · 2021 · Cancer Medicine
20. Diversity Forests: Using Split Sampling to Enable Innovative Complex Split Procedures in Random Forests
    Roman Hornung · 2021 · SN Computer Science
21. Machine Learning Support for Decision-Making in Kidney Transplantation: Step-by-step Development of a Technological Solution
    François-Xavier Paquette, Amir Ghassemi, Olga Bukhtiyarova, Moustapha Cissé, Natanael Gagnon, Alexia Della Vecchia, Hobivola A Rabearivelo, Youssef Loudiyi · 2022 · JMIR Medical Informatics
22. Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning
    Onur Dereli, Ceyda Oǧuz, Mehmet Gönen · 2019 · Bioinformatics
23. Artificial intelligence applications to genomic data in cancer research: a review of recent trends and emerging areas
    Maria Frasca, Davide La Torre, Marco Repetto, Valentina De Nicolò, Gabriella Pravettoni, Ilaria Cutica · 2024 · Discover Analytics
24. Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data
    Anjana Eledath Kolasseri, B Venkataramana · 2024 · Scientific Reports
25. Predictors of time to initiation of symptomatic therapy in early Parkinson's disease
    Tanya Simuni, Jeffrey D. Long, Chelsea Caspell‐Garcia, Christopher S. Coffey, Shirley Lasch, Caroline M. Tanner, Danna L. Jennings, Karl D. Kieburtz, KENNETH L. MAREK, the PPMI Investigators · 2016 · Annals of Clinical and Translational Neurology
26. A Fit-Fat Index for Predicting Incident Diabetes in Apparently Healthy Men: A Prospective Cohort Study
    Robert Alan Sloan, Benjamin Adam Haaland, Susumu S. Sawada, I-min M Lee, Xuemei Sui, Duck-chul Lee, Yassine Ridouane, Falk Müller‐Riemenschneider, Steven N. Blair · 2016 · PLoS ONE
27. Estimating an Individual’s Probability of Revision Surgery After Knee Replacement: A Comparison of Modeling Approaches Using a National Data Set
    Parham Aram, Lea Trela-Larsen, Adrian E. Sayers, Andrew F. Hills, Ashley William Blom, Eugene Vincent McCloskey, Visakan Kadirkamanathan, Jeremy Mark Wilkinson · 2018 · American Journal of Epidemiology
28. Breast Cancer Risk and Insulin Resistance: Post Genome-Wide Gene–Environment Interaction Study Using a Random Survival Forest
    Su Yon Jung, Jeanette C. Papp, Eric M. Sobel, Herbert Yu, Zuo‐Feng Zhang · 2019 · Cancer Research
29. Integrating Tumor and Nodal Imaging Characteristics at Baseline and Mid-Treatment Computed Tomography Scans to Predict Distant Metastasis in Oropharyngeal Cancer Treated With Concurrent Chemoradiotherapy
    Jia Wu, Michael F. Gensheimer, Nasha Zhang, Fei Han, Rachel Liang, Yushen Qian, Carrie Zhang, Nancy J. Fischbein, Erqi Liu Pollom, Beth Michelle Beadle et al. · 2019 · International Journal of Radiation Oncology*Biology*Physics
30. Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model
    Xiaoshuai Zhang, Fang Tang, Jiadong Ji, Wenting Han, Peng Lü · 2019 · Clinical Epidemiology
31. Tree aggregation for random forest class probability estimation
    Andrew J. Sage, Ulrike Genschel, Dan S. Nettleton · 2020 · Statistical Analysis and Data Mining The ASA Data Science Journal
32. Machine learning approaches to study glioblastoma: A review of the last decade of applications
    Jessica Valdebenito, Felipe Medina · 2019 · Cancer Reports
33. Improving the Accuracy and Transparency of Underwriting with Artificial Intelligence to Transform the Life‐Insurance Industry
    Marc E. Maier, Hayley Carlotto, Sara Saperstein, Freddie Sanchez, Sherriff Balogun, Sears Merritt · 2020 · AI Magazine
34. Identification of important risk factors for all-cause mortality of acquired long QT syndrome patients using random survival forests and non-negative matrix factorization
    Cheng Chen, Jiandong Zhou, Haixu Yu, Qingpeng Zhang, Lianjun Gao, Xiaomeng Yin, Yingxue Dong, Yajuan Lin, Daobo Li, Yiheng Yang et al. · 2020 · Heart Rhythm
35. Estimation of asphaltene adsorption on MgO nanoparticles using ensemble learning
    Syed Imran Ali, Shaine Mohammadali Lalji, Javed Haneef, Usama Ahsan, Muhammad Arqam Khan, Nimra Yousaf · 2020 · Chemometrics and Intelligent Laboratory Systems
36. Secretome of Activated Fibroblasts Induced by Exosomes for the Discovery of Biomarkers in Non‐Small Cell Lung Cancer
    Jian Zhang, Bin Fu, Meng Li, Shuangli Mi · 2020 · Small
37. Predictors of mortality among long‐term care residents with SARS‐CoV ‐2 infection
    Douglas S. Lee, Shihao Ma, Anna Chu, Chloe X. Wang, Xuesong Wang, Peter C. Austin, Finlay Aleck McAlister, Sunil V. Kalmady, Moira K. Kapral, Padma Kaul et al. · 2021 · Journal of the American Geriatrics Society
38. What Tears Couples Apart: A Machine Learning Analysis of Union Dissolution in Germany
    Bruno Arpino, Marco Le Moglie, Letizia Mencarini · 2021 · Demography
39. Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context
    Daniel A. Gutierrez-Pachas, Germain Garcia-Zanabria, Ernesto Cuadros-Vargas, Guillermo Cámara-Chávez, Erick Gomez-Nieto · 2023 · Education Sciences
40. Statistical models versus machine learning for competing risks: development and validation of prognostic models
    Georgios Kantidakis, Hein Putter, Saskia Litière, Marta F. Fiocco · 2023 · BMC Medical Research Methodology
41. Prediction of 90Y Radioembolization Outcome from Pretherapeutic Factors with Random Survival Forests
    Michael Ingrisch, Franziska Schoeppe, Karolin Johanna Paprottka, Matthias Philipp Fabritius, Frederik Franz Strobl, Enrico Narciso De Toni, Harun Ilhan, Andrei S. Todica, Marlies Michl, Philipp Marius Paprottka · 2017 · Journal of Nuclear Medicine
42. Asymptotic properties of high-dimensional random forests
    Chien-Ming Chi, Patrick Vossler, Yingying Fan, Jinchi Lv · 2022 · The Annals of Statistics
43. Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data
    Chang Hu, Ravishankar K. Iyer, Brian D. Juran, Bryan M. McCauley, Elizabeth J. Atkinson, John E. Eaton, Ahmad Hassan Ali, Konstantinos N. Lazaridis · 2023 · BMC Gastroenterology
44. Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction
    Joel E. Tosado, Luka Zdilar, Hesham M. Elhalawani, Baher Elgohari, David M. Vock, G. Elisabeta Marai, Clifton David Fuller, Abdallah Mohamed, Guadalupe M. Canahuate · 2020 · Scientific Reports
45. Similar long-term survival after isolated bioprosthetic versus mechanical aortic valve replacement: A propensity-matched analysis
    Tamer Attia, Yanzhi Yang, Lars Georg Svensson, Andrew J. Toth, Jeevanantham Rajeswaran, Eugene H. Blackstone, Douglas R. Johnston, Rashed Mahboubi, Mona Kakavand, Edward G. Soltesz et al. · 2021 · Journal of Thoracic and Cardiovascular Surgery
46. Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease
    Jihye Yun, Young Hoon Cho, Sang Min Lee, Jeongeun Hwang, Jae Seung Lee, Yeon‐Mok Oh, Sang‐Do Lee, Li‐Cher Loh, Choo-Khoon Ong, Joon Beom Seo et al. · 2021 · Scientific Reports
47. Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI
    Santiago Cepeda, Ángel Pérez‐Núñez, Sergio García-García, Daniel García‐Pérez, Ignacio Arrese, Luis Jimenez Roldan, Manuel García-Galindo, P. González, María Velasco-Casares, Tomas Zamora et al. · 2021 · Cancers
48. Variability of soil mapping accuracy with sample sizes, modelling methods and landform types in a regional case study
    Xiaolin Sun, Yu-Qing Lai, Xiaogang Ding, Yunjin Wu, Hui-Li Wang, Chunfa Wu · 2022 · CATENA
49. BITES: balanced individual treatment effect for survival data
    Stefan Schrod, A Schäfer, Susanne Solbrig, Robert Lohmayer, Wolfram Gronwald, Peter Josef Oefner, Tim Beißbarth, Rainer Spang, Helena U. Zacharias, Michael Altenbuchinger · 2022 · Bioinformatics
50. Comparing the Performance of Published Risk Scores in Brugada Syndrome: A Multi-center Cohort Study
    Sharen Lee, Jiandong Zhou, Cheuk To Chung, O. Lee, George Bazoukis, Κonstantinos P. Letsas, Wing Tak Wong, Ian Chi Kei Wong, Ngai Shing Mok, Tong Liu et al. · 2022 · Current Problems in Cardiology
51. Deep multimodal graph-based network for survival prediction from highly multiplexed images and patient variables
    Xiaohang Fu, Ellis Patrick, Jean Yee Hwa Yang, Dagan D. Feng, Jinman Kim · 2023 · Computers in Biology and Medicine
52. A prediction-based alternative to P values in regression models
    Min Lü, Hemant Ishwaran · 2017 · Journal of Thoracic and Cardiovascular Surgery
53. Metabolomic characterisation of progression and spontaneous regression of melanoma in the melanoma-bearing Libechov minipig model
    Martin Kertys, Marián Grendár, Vratislav Horák, Nela Žideková, Helena Kupcová Skalníková, Juraj Mokrý, Erika Halášová, Ján Strnádel · 2021 · Melanoma Research
54. Counterfactual Phenotyping with Censored Time-to-Events
    Chirag Nagpal, Mononito Goswami, Keith Dufendach, Artur W. Dubrawski · 2022 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
55. Accelerated and Interpretable Oblique Random Survival Forests
    Byron C. Jaeger, Sawyer Welden, Kristin Macfarlane Lenoir, Jaime Lynn Speiser, Matthew W. Segar, Ambarish Pandey, Nicholas M. Pajewski · 2023 · Journal of Computational and Graphical Statistics
56. Weighted gene coexpression network analysis and machine learning reveal oncogenome associated microbiome plays an important role in tumor immunity and prognosis in pan-cancer
    Shi-Wei Guan, Quan Lin, Xidong Wu, Haibo Yu · 2023 · Journal of Translational Medicine
57. Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer’s disease
    Alessia Sarica, Federica Aracri, Maria Giovanna Bianco, Fulvia Arcuri, Andrea Quattrone, Aldo Quattrone, Alzheimer’s Disease Neuroimaging Initiative · 2023 · Brain Informatics
58. Cardiovascular Risk Stratification of Patients Undergoing Hematopoietic Stem Cell Transplantation: The CARE‐BMT Risk Score
    Alexi L. Vasbinder, Tonimarie Catalan, Elizabeth L. Anderson, Catherine Chu, Megan Kotzin, Danielle A. Murphy, Halle Cheplowitz, Kristen Machado Diaz, Brayden Bitterman, Ian Pizzo et al. · 2023 · Journal of the American Heart Association
59. Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis
    Yinan Huang, Shadi Bazzazzadehgan, Jieni Li, Arman Arabshomali, Mai Li, Kaustuv Bhattacharya, John P. Bentley · 2025 · BMC Medical Research Methodology
60. KIR and HLA Interactions Are Associated With Control of Primary CMV Infection in Solid Organ Transplant Recipients
    David van Duin, Robin Kimiko Avery, Sonali Hemachandra, Belinda Yen‐Lieberman, A. Zhang, A. Jain, Robert Sam Butler, John Barnard, Jesse D. Schold, John J. Fung et al. · 2013 · American Journal of Transplantation
61. Research and application of random forest model in mining automobile insurance fraud
    Yaqi Li, Chun Gang Yan, Wei Liu, Maozhen Li · 2016 · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
62. MicroRNA signature constituted of miR-30d, miR-93, and miR-181b is a promising prognostic marker in primary central nervous system lymphoma
    Yasuo Takashima, Atsushi Kawaguchi, Yasuo Iwadate, Hiroaki Hondoh, Junya Fukai, Koji Kajiwara, Azusa Hayano, Ryuya Yamanaka · 2019 · PLoS ONE
63. A General Machine Learning Framework for Survival Analysis
    Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl · 2021 · Lecture notes in computer science
64. Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates
    Shu Jiang, Yijun Xie, Graham A. Colditz · 2020 · Journal of the Royal Statistical Society Series C (Applied Statistics)
65. Two-stage Cox-nnet: biologically interpretable neural-network model for prognosis prediction and its application in liver cancer survival using histopathology and transcriptomic data
    Zhucheng Zhan, Zheng Jing, Bing He, Noshad Hosseini, Maria A. Westerhoff, Eun-Young Karen Choi, Lana Xia Garmire · 2021 · NAR Genomics and Bioinformatics
66. Clinical outcomes and predictors of restenosis in patients with femoropopliteal artery disease treated using polymer-coated paclitaxel-eluting stents or drug-coated balloons
    Naoki Yoshioka, Takahiro Tokuda, Akio Kôyama, Takehiro Yamada, Ryusuke Nishikawa, Kiyotaka Shimamura, Kensuke Takagi, Yasuhiro Morita, Akihito Tanaka, Hideki Ishii et al. · 2021 · Heart and Vessels
67. How Much Delay Matters? How Time to Treatment Impacts Overall Survival in Early Stage Lung Cancer
    Andrew Tang, Usman Ahmad, Siva Raja, Alejandro Bribriesco, Monisha Sudarshan, Jesse M.P. Rappaport, Alok A. Khorana, Eugene H. Blackstone, Sudish C. Murthy, Daniel Philip Raymond · 2021 · Annals of Surgery
68. Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach
    Gagnon-Sanschagrin, Patrick, Schein, Jeff, Urganus, Annette, Serra, Elizabeth, Liang, Yawen, Musingarimi, Primrose, Cloutier, Martin, Guérin, Annie, Davis, Lori L. · 2022 · BMC Psychiatry
69. Comparison of a chronic kidney disease predictive model for type 2 diabetes mellitus in Malaysia using Cox regression versus machine learning approach
    Ruth Sim, Chun Wie Chong, Navin Kumar Loganadan, Noor Lita Adam, Zanariah Binti Hussein, Shaun Wen Huey Lee · 2022 · Clinical Kidney Journal
70. Deep learning-based personalised outcome prediction after acute ischaemic stroke
    Doo-Young Kim, Kang‐Ho Choi, Jahae Kim, Jina Hong, Seong‐Min Choi, Man‐Seok Park, Ki-Hyun Cho · 2023 · Journal of Neurology Neurosurgery & Psychiatry
71. Linking soil health indicators to management history and soybean yield
    Lindsay Chamberlain Malone, Matthew D. Ruark, Christopher J. Kucharik, Thea L. Whitman, Shawn P. Conley · 2023 · Field Crops Research
72. 18F‑FDG PET/CT based radiomics features improve prediction of prognosis: multiple machine learning algorithms and multimodality applications for multiple myeloma
    Haoshu Zhong, Delong Huang, Junhao Wu, Xiaomin Chen, Yue Chen, Chunlan Huang · 2023 · BMC Medical Imaging
73. Data-Driven Lead-Acid Battery Prognostics Using Random Survival Forests
    Erik Frisk, Mattias Krysander, Emil Larsson · 2014 · Annual Conference of the PHM Society
74. Outcomes of Dissection Angles as Predictor of Restenosis after Drug-Coated Balloon Treatment
    Amane Kozuki, Mitsuyoshi Takahara, Masahiro Shimizu, Yoichi Kijima, Ryoji Nagoshi, Ryudo Fujiwara, Hiroyuki Shibata, Atsushi Suzuki, Fumitaka Soga, Tomohiro Miyata et al. · 2020 · Journal of Atherosclerosis and Thrombosis
75. Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research
    Sun, Kai, Roy, Arkajyoti, Tobin, Joshua M. · 2024 · Journal of Critical Care
76. JCDSA: a joint covariate detection tool for survival analysis on tumor expression profiles
    Yiming Wu, Yanan Liu, Yueming Wang, Yan Shi, Xudong Zhao · 2018 · BMC Bioinformatics
77. Mineralocorticoid Receptor Antagonism Prevents Aortic Plaque Progression and Reduces Left Ventricular Mass and Fibrosis in Patients With Type 2 Diabetes and Chronic Kidney Disease: The MAGMA Trial
    Sanjay Rajagopalan, Mirela A. Dobre, Jean-Eudes Dazard, Armando Vergara-Martel, Kim Alexander Connelly, Michael E. Farkouh, Juan Gaztañaga, Heather Conger, Ann M. Dever, Laleh Razavi-Nematollahi et al. · 2024 · Circulation
78. An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation
    Shiva Arjmandmazidi, Hamid Reza Heidari, Tohid Ghasemnejad, Zeinab Mori, Leila Molavi, Amir Meraji, Shadi Kaghazchi, Elnaz Mehdizadeh Aghdam, Soheila Montazersaheb · 2025 · Journal of Translational Medicine
79. Optimizing predictive performance of criminal recidivism models using registration data with binary and survival outcomes
    Nikolaj Tollenaar, P.G.M. van der Heijden · 2019 · PLoS ONE
80. Super Learner for Survival Data Prediction
    Marzieh K. Golmakani, Eric C. Polley · 2020 · The International Journal of Biostatistics
81. Semiparametric analysis of clustered interval‐censored survival data using soft Bayesian additive regression trees (SBART)
    Piyali Basak, Antonio R. Linero, Debajyoti Sinha, Stuart R. Lipsitz · 2021 · Biometrics
82. Developing an Improved Statistical Approach for Survival Estimation in Bone Metastases Management: The Bone Metastases Ensemble Trees for Survival (BMETS) Model
    Sara Rachel Alcorn, Jacob Fiksel, Jean L. Wright, Christen R. Elledge, Thomas J. Smith, Powell Perng, Sarah Saleemi, Todd McNutt, Theodore L. DeWeese, Scott L. Zeger · 2020 · International Journal of Radiation Oncology*Biology*Physics
83. Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing
    Mohit Kumar Pandey, Zhuoran Xu, Evan Sholle, Gabriel Maliakal, Gurpreet Singh, Zahra Fatima, Daria Larine, Benjamin C. Lee, Jing Wang, Alexander R. van Rosendael et al. · 2020 · PLoS ONE
84. BAYESIAN NONPARAMETRIC CROSS-STUDY VALIDATION OF PREDICTION METHODS
    Lorenzo Trippa, Levi Waldron, Curtis Huttenhower, Giovanni Parmigiani · 2015 · CUNY Academic Works (City University of New York)
85. Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review
    Ankush D. Jamthikar, Anudeep Puvvula, Deep Gupta, Amer M. Johri, Vijay Nambi, Narendra Nath Khanna, Luca Saba, Sophie I. Mavrogeni, John R. Laird, Gyan Pareek et al. · 2021 · International Angiology
86. A phenotypic risk score for predicting mortality in sickle cell disease
    Vandana Sachdev, Xin Tian, Yuan Gu, James Nichols, Stanislav Sidenko, Wen Li, Andrea Beri, W. Austin Layne, Darlene T. Allen, Colin O. Wu et al. · 2021 · British Journal of Haematology
87. Paediatric/young versus adult patients with long QT syndrome
    Sharen Lee, Jiandong Zhou, Kamalan Jeevaratnam, Wing Tak Wong, Ian Chi Kei Wong, Chloe Miu Mak, Ngai Shing Mok, Tong Liu, Qingpeng Zhang, Gary Tse · 2021 · Open Heart
88. Supervised Two-Dimensional Functional Principal Component Analysis with Time-to-Event Outcomes and Mammogram Imaging Data
    Shu Jiang, Jiguo Cao, BERNARD A. ROSNER, Graham A. Colditz · 2021 · Biometrics
89. Establishment of a predictive model for GVHD-free, relapse-free survival after allogeneic HSCT using ensemble learning
    Makoto Iwasaki, Junya Kanda, Yasuyuki Arai, Tadakazu Kondo, Takayuki Ishikawa, Yasunori Ueda, Kazunori Imada, Takashi Akasaka, Akihito Yonezawa, Kazuhiro Yago et al. · 2021 · Blood Advances
90. Matching-adjusted indirect treatment comparison of chimeric antigen receptor T-cell therapies for third-line or later treatment of relapsed or refractory large B-cell lymphoma: lisocabtagene maraleucel versus tisagenlecleucel
    Guillaume Cartron, Christopher Paul Fox, Fei‐Fei Liu, Ana Kostić, Jens Hasskarl, Daniel Li, Ashley Bonner, Yixie Zhang, David G. Maloney, John G. Kuruvilla · 2022 · Experimental Hematology and Oncology
91. Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis
    Adrián Mosquera-Orgueira, Manuel Pérez‐Encinas, Alberto Hernández‐Sánchez, Teresa González‐Martínez, Eduardo Arellano‐Rodrigo, Javier Martinez Martínez Elicegui, Ángela Villaverde-Ramiro, José María Raya, Rosa María Ayala, Francisca Ferrer‐Marín et al. · 2022 · HemaSphere
92. Time to Awakening and Self-Fulfilling Prophecies After Cardiac Arrest
    Jonathan Elmer, Michael Christopher Kurz, Patrick J. Coppler, Alexis Steinberg, Stephanie C. DeMasi, Maria De‐Arteaga, Noah Simon, Vladimir Zadorozhny, Katharyn L. Flickinger, Clifton W. Callaway · 2023 · Critical Care Medicine
93. Specific Cryptosporidium antigens associate with reinfection immunity and protection from cryptosporidiosis
    Carol A. Gilchrist, Joseph J. Campo, Jozelyn V Pablo, Jennie Z. Ma, Andy Teng, Amit Oberai, Adam D. Shandling, Masud Alam, Mamun Kabir, A. S. G. Faruque et al. · 2023 · Journal of Clinical Investigation
94. Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study
    Zahra Mansouri, Yazdan Salimi, Mehdi Amini, Ghasem Hajianfar, Mehrdad Oveisi, Isaac Shiri, Habib Zaidi · 2024 · Radiation Oncology
95. Application of Random Forest Survival Models to Increase Generalizability of Decision Trees: A Case Study in Acute Myocardial Infarction
    Iman Yosefian, Ehsan Mosa Farkhani, Mohammad Reza Baneshi · 2015 · Computational and Mathematical Methods in Medicine
96. Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit
    Joeri Ruyssinck, Joachim van der Herten, Rein Houthooft, Femke Ongenae, Ivo Couckuyt, Bram Gadeyne, Kirsten E. Colpaert, Johan M. A. Decruyenaere, Filip De Turck, Tom Dhaene · 2016 · Computational and Mathematical Methods in Medicine
97. Stable Cox regression for survival analysis under distribution shifts
    Shaohua Fan, Renzhe Xu, Qian Dong, Yue He, Cheng Chang, Peng Cui · 2024 · Nature Machine Intelligence
98. Bayesian nonparametric cross-study validation of prediction methods
    Lorenzo Trippa, Levi Waldron, Curtis Huttenhower, Giovanni Parmigiani · 2015 · The Annals of Applied Statistics
99. Deep learning for survival outcomes
    Jon Arni Steingrimsson, Samantha Morrison · 2020 · Statistics in Medicine
100. Predictors of Survival After Liver Transplantation in Patients With the Highest Acuity (MELD ≥40)
     Michael D. Evans, Jessica Diaz, Anna Adamusiak, Timothy L. Pruett, Varvara A. Kirchner, Raja Kandaswamy, Vanessa R. Humphreville, Thomas M. Leventhal, Jeffrey O. Grosland, David M. Vock et al. · 2020 · Annals of Surgery