R/ischemic_stroke.R

In modeldata: Data Sets Useful for Modeling Examples

#' Clinical data used to predict ischemic stroke
#'
#' @description
#' A data set to predict a binary outcome using imaging and patient data.
#'
#' @name ischemic_stroke
#' @aliases ischemic_stroke
#' @docType data
#' @return \item{ischemic_stroke}{a tibble}
#'
#' @details
#' These data were gathered to predict patient risk for ischemic stroke. A
#' historical set of patients with a range of carotid artery blockages were
#' selected. The data consisted of 126 patients, 44 of which had blockages
#' greater than 70%. All patients had undergone Computed Tomography Angiography
#' (CTA) to generate a detailed three-dimensional visualization and
#' characterization of the blockage. These images were then analyzed in order to
#' compute several features related to the disease, including: percent stenosis,
#' arterial wall thickness, and tissue characteristics such as lipid-rich
#' necrotic core and calcification.
#'
#' The group of patients in this study also had follow-up information on
#' whether or not a stroke occurred at a subsequent point in time. The data for
#' each patient also included commonly collected clinical characteristics for
#' risk of stroke such as whether or not the patient had atrial fibrillation,
#' coronary artery disease, and a history of smoking. Demographics of gender and
#' age were included as well. These readily available risk factors can be
#' thought of as another potentially useful predictor set that can be evaluated.
#' In fact, this set of predictors should be evaluated first to assess their
#' ability to predict stroke since these predictors are easy to collect, are
#' acquired at patient presentation, and do not require an expensive imaging
#' technique.
#'
#' Columns:
#' \itemize{
#'  \item \code{stroke}:  factor (levels: 'yes' and 'no')
#'  \item \code{nascet_scale}:  numeric
#'  \item \code{calc_vol}:  numeric
#'  \item \code{calc_vol_prop}:  numeric
#'  \item \code{matx_vol}:  numeric
#'  \item \code{matx_vol_prop}:  numeric
#'  \item \code{lrnc_vol}:  numeric
#'  \item \code{lrnc_vol_prop}:  numeric
#'  \item \code{max_calc_area}:  numeric
#'  \item \code{max_calc_area_prop}:  numeric
#'  \item \code{max_dilation_by_area}:  numeric
#'  \item \code{max_matx_area}:  numeric
#'  \item \code{max_matx_area_prop}:  numeric
#'  \item \code{max_lrnc_area}:  numeric
#'  \item \code{max_lrnc_area_prop}:  numeric
#'  \item \code{max_max_wall_thickness}:  numeric
#'  \item \code{max_remodeling_ratio}:  numeric
#'  \item \code{max_stenosis_by_area}:  numeric
#'  \item \code{max_wall_area}:  numeric
#'  \item \code{wall_vol}:  numeric
#'  \item \code{max_stenosis_by_diameter}:  numeric
#'  \item \code{age}:  integer
#'  \item \code{male}:  integer
#'  \item \code{smoking_history}:  integer
#'  \item \code{atrial_fibrillation}:  integer
#'  \item \code{coronary_artery_disease}:  integer
#'  \item \code{diabetes_history}:  integer
#'  \item \code{hypercholesterolemia_history}:  integer
#'  \item \code{hypertension_history}:  integer
#' }
#' @source
#' Kuhn, Max, and Kjell Johnson. _Feature Engineering and Selection: A Practical
#' Approach for Predictive Models_. Chapman and Hall/CRC, 2019.
#'
#' @examples
#' data(ischemic_stroke)
#' str(ischemic_stroke)
#'
NULL