Results 71 to 80 of about 338,729 (282)

Impact of Asymptomatic Intracranial Hemorrhage on Outcome After Endovascular Stroke Treatment

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Endovascular treatment (EVT) achieves high rates of recanalization in acute large‐vessel occlusion (LVO) stroke, but functional recovery remains heterogeneous. While symptomatic intracranial hemorrhage (sICH) has been well studied, the prognostic impact of asymptomatic intracranial hemorrhage (aICH) after EVT is less certain ...
Shihai Yang, Chengsong Yue, Zhuang Li, Xiaolei Shi, Haoxuan Zhu, Jie Yang, Linyu Li, Jiaxing Song, Shitao Fan, Gaoming Li, Wenzhe Sun, Zhixian Ye, Jiacheng Huang, Dahong Yang, Jinfu Ma, Xu Xu, Zhixi Wang, Lilan Wang, Guojian Liu, Fengli Li, Changwei Guo, Li Qi, Wenjie Zi   +22 more
wiley   +1 more source

Advancing Psychological Research With Random Forests: A Review of Methods, Tools, and Applications

Advances in Methods and Practices in Psychological Science
Contemporary psychological research increasingly involves machine-learning techniques, including random forests, for their capability in analyzing complex, high-dimensional data sets and modeling nonlinear predictive relations.
Yi Feng, Han Du, Jiarui Song, Yina Sun, Yiting Wang, Aedan Joel   +5 more
doaj   +1 more source

Heterogeneous Random Forest

CoRR
Random forest (RF) stands out as a highly favored machine learning approach for classification problems. The effectiveness of RF hinges on two key factors: the accuracy of individual trees and the diversity among them. In this study, we introduce a novel approach called heterogeneous RF (HRF), designed to enhance tree diversity in a meaningful way ...
Ye-eun Kim, Seoung Yun Kim, Hyunjoong Kim   +2 more
openaire   +2 more sources

On Oblique Random Forests [PDF]

, 2011
In his original paper on random forests, Breiman proposed two different decision tree ensembles: one generated from "orthogonal" trees with thresholds on individual features in every split, and one from "oblique" trees separating the feature space by randomly oriented hyperplanes.
Bjoern H. Menze, B. Michael Kelm, Daniel Nicolas Splitthoff, Ullrich Köthe, Fred A. Hamprecht   +4 more
openaire   +1 more source

Value of MRI Outcomes for Preventive and Early‐Stage Trials in Spinocerebellar Ataxias 1 and 3

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the value of MRI outcomes as endpoints for preventive and early‐stage trials of two polyglutamine spinocerebellar ataxias (SCAs). Methods A cohort of 100 participants (23 SCA1, 63 SCA3, median Scale for the Assessment and Rating of Ataxia (SARA) score = 5, 42% preataxic, and 14 gene‐negative controls) was scanned at 3T up ...
Thiago J. R. Rezende, Emilien Petit, Young Woo Park, Sophie Tezenas du Montcel, James M. Joers, Jonathan M. DuBois, H. Moore Arnold, Michal Povazan, Ipek Özdemir, Guita Banan, Romain Valabregue, Philipp Ehses, Jennifer Faber, Chiadi U. Onyike, Peter B. Barker, Jeremy D. Schmahmann, Eva‐Maria Ratai, Sub H. Subramony, Thomas H. Mareci, Khalaf O. Bushara, Henry Paulson, Thomas Klockgether, Alexandra Durr, Tetsuo Ashizawa, Christophe Lenglet, Gülin Öz, READISCA Investigators   +26 more
wiley   +1 more source

K-nearest Neighbor Search by Random Projection Forests

, 2018
K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose ...
Li, Zhenpeng, Wang, Honggang, Wang, Jin, Wang, Yingjie, Yan, Donghui   +4 more
core   +1 more source

Banzhaf Random Forests

CoRR, 2015
Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees. However, the theory of random forests has long been outpaced by their application. In this paper, we propose a novel random forests algorithm based on cooperative game theory.
Jianyuan Sun, Guoqiang Zhong 0001, Junyu Dong, Yajuan Cai   +3 more
openaire   +2 more sources

Joints in Random Forests

CoRR, 2020
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist. Due to their discriminative nature, however, they lack principled methods to process inputs with missing features or to detect outliers, which requires pairing them with ...
Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos   +2 more
openaire   +4 more sources

Fluid Biomarkers of Disease Burden and Cognitive Dysfunction in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Identifying objective biomarkers for progressive supranuclear palsy (PSP) is crucial to improving diagnosis and establishing clinical trial and treatment endpoints. This study evaluated fluid biomarkers in PSP versus controls and their associations with regional 18F‐PI‐2620 tau‐PET, clinical, and cognitive outcomes.
Roxane Dilcher, Charles B. Malpas, Stuart J. McDonald, William T. O’Brien, Craig Despott, Kelly L. Bertram, Matthew P. Pase, Lukas Frontzkowski, Meng Law, Terence J. O’Brien, Lucy Vivash   +10 more
wiley   +1 more source

Temperature impact on the economic growth effect: method development and model performance evaluation with subnational data in China

EPJ Data Science, 2023
Temperature-economic growth relationships are computed to quantify the impact of climate change on the economy. However, model performance and differences of predictions among research complicate the use of climate econometric estimation.
Yu Song, Zhihua Pan, Fei Lun, Buju Long, Siyu Liu, Guolin Han, Jialin Wang, Na Huang, Ziyuan Zhang, Shangqian Ma, Guofeng Sun, Cong Liu   +11 more
doaj   +1 more source