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Papers: 10.3390/jcm7100322

https://doi.org/10.3390/jcm7100322

Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery

Cited by: 100
Author(s): Hyung Chul Lee, Hyun Kyu Yoon, Karam Nam, Youn Joung Cho, Tae Kyoung Kim, Won Ho Kim, Jae Hyon Bahk
Published: almost 8 years ago

Software Mentions 12

cran: e1071
Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien
Papers that mentioned: 419
Very Likely Science (85)
cran: h2o
R Interface for the 'H2O' Scalable Machine Learning Platform
Papers that mentioned: 43
Very Likely Science (100)
cran: kernlab
Kernel-Based Machine Learning Lab
Papers that mentioned: 157
Very Likely Science (85)
cran: neuralnet
Training of Neural Networks
Papers that mentioned: 37
Very Likely Science (100)
cran: nnet
Feed-Forward Neural Networks and Multinomial Log-Linear Models
Papers that mentioned: 296
Very Likely Science (93)
cran: randomForest
Breiman and Cutler's Random Forests for Classification and Regression
Papers that mentioned: 1,447
Very Likely Science (100)
cran: ROSE
Random Over-Sampling Examples
Papers that mentioned: 131
Very Likely Science (75)
cran: rpart
Recursive Partitioning and Regression Trees
Papers that mentioned: 431
Very Likely Science (100)
cran: UBL
An Implementation of Re-Sampling Approaches to Utility-Based Learning for Both Classification and Regression Tasks
Papers that mentioned: 3
Very Likely Science (80)
pypi: h2o
H2O, Fast Scalable Machine Learning, for python
Papers that mentioned: 43
Very Likely Science (100)
pypi: neuralnet
A high-level library on top of Theano.
Papers that mentioned: 37
Very Likely Science (85)
pypi: Tree
A package for creating and drawing trees
Papers that mentioned: 286
Very Likely Science (80)