124 related articles for article (PubMed ID: 31460679)
1. Estimating individual treatment effects by gradient boosting trees.
Sugasawa S; Noma H
Stat Med; 2019 Nov; 38(26):5146-5159. PubMed ID: 31460679
[TBL] [Abstract][Full Text] [Related]
2. A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data.
Zhang P; Ma J; Chen X; Shentu Y
Stat Med; 2020 Dec; 39(28):4133-4146. PubMed ID: 32786155
[TBL] [Abstract][Full Text] [Related]
3. Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort.
Fang G; Annis IE; Elston-Lafata J; Cykert S
J Am Med Inform Assoc; 2019 Oct; 26(10):977-988. PubMed ID: 31220274
[TBL] [Abstract][Full Text] [Related]
4. Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases.
Wendling T; Jung K; Callahan A; Schuler A; Shah NH; Gallego B
Stat Med; 2018 Oct; 37(23):3309-3324. PubMed ID: 29862536
[TBL] [Abstract][Full Text] [Related]
5. Random forests of interaction trees for estimating individualized treatment effects in randomized trials.
Su X; Peña AT; Liu L; Levine RA
Stat Med; 2018 Jul; 37(17):2547-2560. PubMed ID: 29707855
[TBL] [Abstract][Full Text] [Related]
6. Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework.
Bussy S; Veil R; Looten V; Burgun A; Gaïffas S; Guilloux A; Ranque B; Jannot AS
BMC Med Res Methodol; 2019 Mar; 19(1):50. PubMed ID: 30841867
[TBL] [Abstract][Full Text] [Related]
7. Building more accurate decision trees with the additive tree.
Luna JM; Gennatas ED; Ungar LH; Eaton E; Diffenderfer ES; Jensen ST; Simone CB; Friedman JH; Solberg TD; Valdes G
Proc Natl Acad Sci U S A; 2019 Oct; 116(40):19887-19893. PubMed ID: 31527280
[TBL] [Abstract][Full Text] [Related]
8. Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.
Liu Y; Wang Y; Kosorok MR; Zhao Y; Zeng D
Stat Med; 2018 Nov; 37(26):3776-3788. PubMed ID: 29873099
[TBL] [Abstract][Full Text] [Related]
9. Predictive analytics with gradient boosting in clinical medicine.
Zhang Z; Zhao Y; Canes A; Steinberg D; Lyashevska O;
Ann Transl Med; 2019 Apr; 7(7):152. PubMed ID: 31157273
[TBL] [Abstract][Full Text] [Related]
10. Nonparametric machine learning for precision medicine with longitudinal clinical trials and Bayesian additive regression trees with mixed models.
Spanbauer C; Sparapani R
Stat Med; 2021 May; 40(11):2665-2691. PubMed ID: 33751659
[TBL] [Abstract][Full Text] [Related]
11. Estimating individualized optimal combination therapies through outcome weighted deep learning algorithms.
Liang M; Ye T; Fu H
Stat Med; 2018 Nov; 37(27):3869-3886. PubMed ID: 30014497
[TBL] [Abstract][Full Text] [Related]
12. Boosting for high-dimensional two-class prediction.
Blagus R; Lusa L
BMC Bioinformatics; 2015 Sep; 16():300. PubMed ID: 26390865
[TBL] [Abstract][Full Text] [Related]
13. Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees.
Logan BR; Sparapani R; McCulloch RE; Laud PW
Stat Methods Med Res; 2019 Apr; 28(4):1079-1093. PubMed ID: 29254443
[TBL] [Abstract][Full Text] [Related]
14. Ensemble survival trees for identifying subpopulations in personalized medicine.
Chen YC; Chen JJ
Biom J; 2016 Sep; 58(5):1151-63. PubMed ID: 27073016
[TBL] [Abstract][Full Text] [Related]
15. The evolution of boosting algorithms. From machine learning to statistical modelling.
Mayr A; Binder H; Gefeller O; Schmid M
Methods Inf Med; 2014; 53(6):419-27. PubMed ID: 25112367
[TBL] [Abstract][Full Text] [Related]
16. A dropout-regularized classifier development approach optimized for precision medicine test discovery from omics data.
Roder J; Oliveira C; Net L; Tsypin M; Linstid B; Roder H
BMC Bioinformatics; 2019 Jun; 20(1):325. PubMed ID: 31196002
[TBL] [Abstract][Full Text] [Related]
17. Machine Learning Algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer's Disease Patients in the General Population.
Uspenskaya-Cadoz O; Alamuri C; Wang L; Yang M; Khinda S; Nigmatullina Y; Cao T; Kayal N; O'Keefe M; Rubel C
J Prev Alzheimers Dis; 2019; 6(3):185-191. PubMed ID: 31062833
[TBL] [Abstract][Full Text] [Related]
18. Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine.
Arora P; Boyne D; Slater JJ; Gupta A; Brenner DR; Druzdzel MJ
Value Health; 2019 Apr; 22(4):439-445. PubMed ID: 30975395
[TBL] [Abstract][Full Text] [Related]
19. Machine learning models in breast cancer survival prediction.
Montazeri M; Montazeri M; Montazeri M; Beigzadeh A
Technol Health Care; 2016; 24(1):31-42. PubMed ID: 26409558
[TBL] [Abstract][Full Text] [Related]
20. Validating effectiveness of subgroup identification for longitudinal data.
Andrews N; Cho H
Stat Med; 2018 Jan; 37(1):98-106. PubMed ID: 28948635
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]