An open API service providing mapping between scientific papers and software projects that are mentioned in them.

Papers: 10.1371/journal.pmed.1002703

https://doi.org/10.1371/journal.pmed.1002703

Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

Cited by: 81
Author(s): Chenxi Huang, Karthik Murugiah, Shiwani Mahajan, Shuxia Li, Sanket S. Dhruva, Julian Haimovich, Yongfei Wang, Wade L. Schulz, Jeffrey M. Testani, F. Perry Wilson, Carlos Mena, Frederick A. Masoudi, John S. Rumsfeld, John A. Spertus, Bobak J. Mortazavi, Harlan M. Krumholz
Published: over 6 years ago

Software Mentions 3

cran: SpecsVerification
Forecast Verification Routines for Ensemble Forecasts of Weather and Climate
Papers that mentioned: 2
Very Likely Science (83)
cran: xgboost
Extreme Gradient Boosting
Papers that mentioned: 207
Very Likely Science (100)
pypi: xgboost
XGBoost Python Package
Papers that mentioned: 207
Very Likely Science (90)