Literature DB >> 22088987

Random Survival Forests.

Jeremy M G Taylor.   

Abstract

Mesh：

Year:  2011        PMID： 22088987     DOI： 10.1097/JTO.0b013e318233d835

Source DB:  PubMed          Journal:  J Thorac Oncol        ISSN： 1556-0864            Impact factor:   15.609


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  16 in total

1.  Using machine learning to examine medication adherence thresholds and risk of hospitalization.

Authors:  Wei-Hsuan Lo-Ciganic; Julie M Donohue; Joshua M Thorpe; Subashan Perera; Carolyn T Thorpe; Zachary A Marcum; Walid F Gellad
Journal:  Med Care       Date:  2015-08       Impact factor: 2.983

2.  Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD.

Authors:  Yiping Lu; Si Wu; Changwan Cui; Miao Yu; Shuang Wang; Yuanyi Yue; Miao Liu; Zhengrong Sun
Journal:  Onco Targets Ther       Date:  2020-10-14       Impact factor: 4.147

3.  Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis.

Authors:  Xin Luo; Xiao Zang; Lin Yang; Junzhou Huang; Faming Liang; Jaime Rodriguez-Canales; Ignacio I Wistuba; Adi Gazdar; Yang Xie; Guanghua Xiao
Journal:  J Thorac Oncol       Date:  2016-11-04       Impact factor: 15.609

4.  Random survival forests using linked data to measure illness burden among individuals before or after a cancer diagnosis: Development and internal validation of the SEER-CAHPS illness burden index.

Authors:  Lisa M Lines; Julia Cohen; Justin Kirschner; Michael T Halpern; Erin E Kent; Michelle A Mollica; Ashley Wilder Smith
Journal:  Int J Med Inform       Date:  2020-10-21       Impact factor: 4.046

5.  Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients.

Authors:  Xiao Huo; Xiaoshuang Zhou; Peng Peng; Mei Yu; Ying Zhang; Jiaxin Yang; Dongyan Cao; Hengzi Sun; Keng Shen
Journal:  Onco Targets Ther       Date:  2021-02-05       Impact factor: 4.147

6.  Random survival forests identify pathways with polymorphisms predictive of survival in KRAS mutant and KRAS wild-type metastatic colorectal cancer patients.

Authors:  Madiha Naseem; Shu Cao; Dongyun Yang; Joshua Millstein; Alberto Puccini; Fotios Loupakis; Sebastian Stintzing; Chiara Cremolini; Ryuma Tokunaga; Francesca Battaglin; Shivani Soni; Martin D Berger; Afsaneh Barzi; Wu Zhang; Alfredo Falcone; Volker Heinemann; Heinz-Josef Lenz
Journal:  Sci Rep       Date:  2021-06-09       Impact factor: 4.379

7.  A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.

Authors:  Justine B Nasejje; Henry Mwambi; Keertan Dheda; Maia Lesosky
Journal:  BMC Med Res Methodol       Date:  2017-07-28       Impact factor: 4.615

8.  Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis.

Authors:  Hongxia Ma; Lihong Tong; Qian Zhang; Wenjun Chang; Fengsen Li
Journal:  Biomed Res Int       Date:  2020-06-08       Impact factor: 3.411

9.  Predicting Intracerebral Hemorrhage Patients' Length-of-Stay Probability Distribution Based on Demographic, Clinical, Admission Diagnosis, and Surgery Information.

Authors:  Li Luo; Xueru Xu; Yan Jiang; Wei Zhu
Journal:  J Healthc Eng       Date:  2019-01-27       Impact factor: 2.682

10.  Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption.

Authors:  Justine B Nasejje; Henry Mwambi
Journal:  BMC Res Notes       Date:  2017-09-07
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