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Papers: 10.1371/journal.pone.0219302

https://doi.org/10.1371/journal.pone.0219302

Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

Cited by: 74
Author(s): Joon Kwon, Kyung Hee Kim, Ki‐Hyun Jeon, Sang Eun Lee, Hae Young Lee, Hyun Jai Cho, Jin Oh Choi, Eun Seok Jeon, Min Seok Kim, Jae Joong Kim, Kyung Kuk Hwang, Shung Chull Chae, Sang Hong Baek, Seok Min Kang, Dong Ju Choi, Byung Su Yoo, Kye Hun Kim, Hyun-Young Park, Myeong Chan Cho, Byung Hee Oh
Published: about 7 years ago

Software Mentions 7

cran: bnlearn
Bayesian Network Structure Learning, Parameter Learning and Inference
Papers that mentioned: 113
Very Likely Science (85)
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: glmulti
Model Selection and Multimodel Inference Made Easy
Papers that mentioned: 93
Very Likely Science (75)
cran: pROC
Display and Analyze ROC Curves
Papers that mentioned: 1,740
Very Likely Science (95)
cran: randomForest
Breiman and Cutler's Random Forests for Classification and Regression
Papers that mentioned: 1,447
Very Likely Science (100)
cran: ROCR
Visualizing the Performance of Scoring Classifiers
Papers that mentioned: 596
Very Likely Science (75)
pypi: bnlearn
Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
Papers that mentioned: 113
Very Likely Science (90)