healthcareai: Tools for Healthcare Machine Learning
Aims to make machine learning in healthcare as easy as possible.
You can develop customized, reliable, high-performance machine learning models with minimal code.
Models are created with automatic preprocessing, hyperparameter tuning, and
algorithm selection (between
'xgboost' Chen, T. & Guestrin, C. (2016) <arXiv:1603.02754>,
'ranger' Wright, M. N., & Ziegler, A. (2017) <doi:10.18637/jss.v077.i01>,
and 'glm' Friedman J, Hastie T, Tibshirani R. (2010) <doi:10.18637/jss.v033.i01>)
so that they can be easily
put into production. Additionally, there are tools to help understand how a model makes its
predictions, select prediction threshholds for operational use, and evaluate model performance over time.
Code uses 'tidyverse' syntax and most methods have an associated visualization.
| Version: |
2.5.1 |
| Depends: |
R (≥ 3.6), methods |
| Imports: |
caret (≥ 6.0.81), cowplot, data.table, dplyr (≥ 1.0.0), e1071, generics, ggplot2, glmnet, lubridate, MLmetrics, purrr, ranger (≥ 0.8.0), recipes (≥ 0.1.3.9002), rlang, ROCR, stringr, tibble (≥ 3.0.0), tidyr, xgboost |
| Suggests: |
covr, DBI, dbplyr, lintr, odbc, testthat |
| Published: |
2022-09-05 |
| Author: |
Levi Thatcher [aut],
Michael Levy [aut],
Mike Mastanduno [aut, cre],
Taylor Larsen [aut],
Taylor Miller [aut],
Rex Sumsion [aut] |
| Maintainer: |
Mike Mastanduno <michael.mastanduno at healthcatalyst.com> |
| BugReports: |
https://github.com/HealthCatalyst/healthcareai-r/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://docs.healthcare.ai/ |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
healthcareai results |
Documentation:
Downloads:
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