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.

Financial Projections and Planning for Health Care Practices
c(
person(given = "Raoul",
family = "Wadhwa",
role = c("aut", "cre"),
email = "raoulwadhwa@gmail.com"),
person(given = "Vigneshwar",
family = "Subramanian",
role = "aut",
email = "vs347@cornell.edu"),
person(given = "Milind",
family = "Desai",
role = "aut"))
Provides a shiny interface for a free, open-source managerial
accounting-like system for health care practices. This package allows
health care administrators to project revenue with monthly adjustments
and procedure-specific boosts up to a 3-year period. Granular data
(patient-level) to aggregated data (department- or hospital-level) can
all be used as valid inputs provided historical volume and revenue
data is available. For more details on managerial accounting
techniques, see Brewer et al. (2015, ISBN:9780078025792).
Raoul Wadhwa [aut, cre],
Vigneshwar Subramanian [aut],
Milind Desai [aut]

c(
person("Lingshu", "Hu", ,
email = "lingshu.hu@hotmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-0304-882X")),
person("Michael W.", "Kearney", ,
email = "kearneymw@missouri.edu", role = c("ctb"),
comment = c(ORCID = "0000-0002-0730-4694")))
Scrape data from Patient Forum <https://patient.info/forums> by entering urls. It will return a data frame containing text, user names, like counts, reply counts, etc.
rvest, magrittr, xml2, purrr, tokenizers, stringr