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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
2009 1
2013 2
2014 1
2015 1
2016 2
2017 5
2018 5
2019 9
2020 16
2021 13
2022 13
2023 6

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71 results

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Calibrating random forests for probability estimation.
Dankowski T, Ziegler A. Dankowski T, et al. Stat Med. 2016 Sep 30;35(22):3949-60. doi: 10.1002/sim.6959. Epub 2016 Apr 13. Stat Med. 2016. PMID: 27074747 Free PMC article.
Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for …
Probabilities can be consistently estimated using random forests. It is, however, unclear how random f …
An independently validated survival nomogram for lower-grade glioma.
Gittleman H, Sloan AE, Barnholtz-Sloan JS. Gittleman H, et al. Neuro Oncol. 2020 May 15;22(5):665-674. doi: 10.1093/neuonc/noz191. Neuro Oncol. 2020. PMID: 31621885 Free PMC article.
Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. ...Factors that increased the probability of survival included grade II tumor, younger …
Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, …
Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction.
Angraal S, Mortazavi BJ, Gupta A, Khera R, Ahmad T, Desai NR, Jacoby DL, Masoudi FA, Spertus JA, Krumholz HM. Angraal S, et al. JACC Heart Fail. 2020 Jan;8(1):12-21. doi: 10.1016/j.jchf.2019.06.013. Epub 2019 Oct 9. JACC Heart Fail. 2020. PMID: 31606361 Free article.
METHODS: The following 5 methods: logistic regression with a forward selection of variables; logistic regression with a lasso regularization for variable selection; random forest (RF); gradient descent boosting; and support vector machine, were used to train models …
METHODS: The following 5 methods: logistic regression with a forward selection of variables; logistic regression with a lasso regularization …
Machine Learning and Statistical Models to Predict Postpartum Hemorrhage.
Venkatesh KK, Strauss RA, Grotegut CA, Heine RP, Chescheir NC, Stringer JSA, Stamilio DM, Menard KM, Jelovsek JE. Venkatesh KK, et al. Obstet Gynecol. 2020 Apr;135(4):935-944. doi: 10.1097/AOG.0000000000003759. Obstet Gynecol. 2020. PMID: 32168227 Free PMC article.
METHODS: Predictive models were constructed and compared using data from 10 of 12 sites in the U.S. Consortium for Safe Labor Study (2002-2008) that consistently reported estimated blood loss at delivery. ...We used logistic regression with and without lasso regular …
METHODS: Predictive models were constructed and compared using data from 10 of 12 sites in the U.S. Consortium for Safe Labor Study ( …
ABC random forests for Bayesian parameter inference.
Raynal L, Marin JM, Pudlo P, Ribatet M, Robert CP, Estoup A. Raynal L, et al. Bioinformatics. 2019 May 15;35(10):1720-1728. doi: 10.1093/bioinformatics/bty867. Bioinformatics. 2019. PMID: 30321307
The approach relies on the random forest (RF) methodology of Breiman (2001) applied in a (non-parametric) regression setting. ...When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statis …
The approach relies on the random forest (RF) methodology of Breiman (2001) applied in a (non-parametric) regression setting. …
Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.
Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu IC, Oberije C, Lustberg T, van Soest J, Hoebers F, Jochems A, El Naqa I, Wee L, Morin O, Raleigh DR, Bots W, Kaanders JH, Belderbos J, Kwint M, Solberg T, Monshouwer R, Bussink J, Dekker A, Lambin P. Deist TM, et al. Med Phys. 2018 Jul;45(7):3449-3459. doi: 10.1002/mp.12967. Epub 2018 Jun 13. Med Phys. 2018. PMID: 29763967 Free PMC article.
General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. ...CONCLUSION: Random forest and elastic net logistic regressi …
General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector mach …
Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population.
Kato H, Hoshino Y, Hidaka N, Ito N, Makita N, Nangaku M, Inoue K. Kato H, et al. J Clin Endocrinol Metab. 2022 Nov 25;107(12):3222-3230. doi: 10.1210/clinem/dgac544. J Clin Endocrinol Metab. 2022. PMID: 36125184 Free PMC article.
We used the NHANES 2003 to 2004 cohort (n = 4096) to train 6 machine-learning prediction models (logistic regression with and without splines, lasso regression, random forest, gradient-boosting machines [GBMs], and SuperLearner). ...Logistic regression models with s …
We used the NHANES 2003 to 2004 cohort (n = 4096) to train 6 machine-learning prediction models (logistic regression with and without spline …
Random forest for prediction of contrast-induced nephropathy following coronary angiography.
Liu Y, Chen S, Ye J, Xian Y, Wang X, Xuan J, Tan N, Li Q, Chen J, Ni Z. Liu Y, et al. Int J Cardiovasc Imaging. 2020 Jun;36(6):983-991. doi: 10.1007/s10554-019-01730-6. Epub 2020 Apr 13. Int J Cardiovasc Imaging. 2020. PMID: 32285318
A total of 3469 patients undergoing PCI/CAG between January 2010 and December 2013 were randomly divided into a training (n = 2428, 70%) and validation data-sets (n = 1041, 30%). Random forest full models were developed using 40 pre-procedural variables, of which 13 …
A total of 3469 patients undergoing PCI/CAG between January 2010 and December 2013 were randomly divided into a training (n = 2428, 70%) and …
Prediction of long-term survival after gastrectomy using random survival forests.
Rahman SA, Maynard N, Trudgill N, Crosby T, Park M, Wahedally H, Underwood TJ, Cromwell DA; NOGCA Project Team and AUGIS. Rahman SA, et al. Br J Surg. 2021 Nov 11;108(11):1341-1350. doi: 10.1093/bjs/znab237. Br J Surg. 2021. PMID: 34297818 Free PMC article.
A total of 2931 patients were included and 29 clinical and pathological variables were considered for their impact on survival. A non-linear random survival forest methodology was then trained and validated internally using bootstrapping with calibration and …
A total of 2931 patients were included and 29 clinical and pathological variables were considered for their impact on survival. A non-linear …
An independently validated nomogram for isocitrate dehydrogenase-wild-type glioblastoma patient survival.
Gittleman H, Cioffi G, Chunduru P, Molinaro AM, Berger MS, Sloan AE, Barnholtz-Sloan JS. Gittleman H, et al. Neurooncol Adv. 2019 May-Dec;1(1):vdz007. doi: 10.1093/noajnl/vdz007. Epub 2019 May 30. Neurooncol Adv. 2019. PMID: 31608326 Free PMC article.
Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. ...Models were internally validated using 10-fold cross-validation and externally validated b …
Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, …
71 results