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Year Number of Results
2009 1
2013 2
2014 1
2015 1
2016 2
2017 5
2018 5
2019 8
2020 6
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31 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
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. ...Performance metrics (AUC, calibration slope and intercept, accurac …
General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector mach …
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.
Nomograms are useful tools for individualized estimation of survival. This study aimed to develop and independently validate a nomogram for IDH-wild-type patients with newly diagnosed GBM. ...Survival was assessed using Cox proportional hazards regression, random su …
Nomograms are useful tools for individualized estimation of survival. This study aimed to develop and independently validate a nomogr …
An independently validated nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825.
Gittleman H, Lim D, Kattan MW, Chakravarti A, Gilbert MR, Lassman AB, Lo SS, Machtay M, Sloan AE, Sulman EP, Tian D, Vogelbaum MA, Wang TJC, Penas-Prado M, Youssef E, Blumenthal DT, Zhang P, Mehta MP, Barnholtz-Sloan JS. Gittleman H, et al. Neuro Oncol. 2017 May 1;19(5):669-677. doi: 10.1093/neuonc/now208. Neuro Oncol. 2017. PMID: 28453749 Free PMC article.
Nomograms are often used for individualized estimation of prognosis. This study aimed to build and independently validate a nomogram to estimate individualized survival probabilities for patients with newly diagnosed GBM, using data from 2 independent NRG Onc …
Nomograms are often used for individualized estimation of prognosis. This study aimed to build and independently validate a nomogram …
Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk.
Dimopoulos AC, Nikolaidou M, Caballero FF, Engchuan W, Sanchez-Niubo A, Arndt H, Ayuso-Mateos JL, Haro JM, Chatterji S, Georgousopoulou EN, Pitsavos C, Panagiotakos DB. Dimopoulos AC, et al. BMC Med Res Methodol. 2018 Dec 29;18(1):179. doi: 10.1186/s12874-018-0644-1. BMC Med Res Methodol. 2018. PMID: 30594138 Free PMC article.
BACKGROUND: The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. ...Three different machine-learning classifiers (k-NN, random forest, and …
BACKGROUND: The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, …
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
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, …
Calibrating an individual-based movement model to predict functional connectivity for little owls.
Hauenstein S, Fattebert J, Grüebler MU, Naef-Daenzer B, Pe'er G, Hartig F. Hauenstein S, et al. Ecol Appl. 2019 Jun;29(4):e01873. doi: 10.1002/eap.1873. Epub 2019 Mar 22. Ecol Appl. 2019. PMID: 30756457
We then include the estimated parameters in a spatially explicit individual-based model (IBM) of little owl dispersal and calibrate further movement parameters using ABC. To derive efficient summary statistics, we use a new dimension reduction method based on ran
We then include the estimated parameters in a spatially explicit individual-based model (IBM) of little owl dispersal and calibrat
Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.
Nanayakkara S, Fogarty S, Tremeer M, Ross K, Richards B, Bergmeir C, Xu S, Stub D, Smith K, Tacey M, Liew D, Pilcher D, Kaye DM. Nanayakkara S, et al. PLoS Med. 2018 Nov 30;15(11):e1002709. doi: 10.1371/journal.pmed.1002709. eCollection 2018 Nov. PLoS Med. 2018. PMID: 30500816 Free PMC article. Clinical Trial.
LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neural network [ANN], and an ensemble) were compared to the APACHE III and Australian and New Zealand Risk of Death (ANZROD) predictions. ...Expl …
LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neura …
Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non-Metastatic Chondrosarcoma from the SEER Database.
Huang R, Sun Z, Zheng H, Yan P, Hu P, Yin H, Zhang J, Meng T, Huang Z. Huang R, et al. Orthop Surg. 2019 Oct;11(5):801-810. doi: 10.1111/os.12521. Orthop Surg. 2019. PMID: 31663279 Free PMC article.
Patients from the database were regarded as the training set, and univariate analysis, Lasso regression and multivariate analysis as well as the random forest were used to explore the predictors and establish nomograms. ...We estimated the discriminative abil …
Patients from the database were regarded as the training set, and univariate analysis, Lasso regression and multivariate analysis as well as …
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