Paper Repository: Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation
by Anton S. Becker · Joseph P. Erinjeri · Joshua Chaim · Nicholas Kastango · Pierre Elnajjar · Hedvig Hricak · H. Alberto Vargas
This is the companion analysis to a paper published in the Journal of Digital Imaging.
To run the analysis in ExamForecast.Rmd
, the following packages need to be installed:
install.packages(
c(
"here",
"kableExtra",
"knitr",
"magrittr",
"prophet",
"readr",
"rmarkdown",
"stringr",
"tidyverse",
"timeDate"
# Recommended:
"rticles"
"skimr"
)
)
The synthetic toy data is provided in the "Data" folder (gzipped csv). It can be read natively by {{readr::read_csv}}
or alternatively unzipped by R's native read.csv
function. To run a prophet forecast with our own data, replace the csv files with your own data with at least two columns: Date
and number of examinations
.
In order to recycle the code from this repository a column modality_code
should be added containing either "CT" or "MRI".
library(dplyr)
here::here("Data", "per_diem_msk.csv.gz") %>%
readr::read_csv(show_col_types = FALSE) %>%
skimr::skim()
For a more comprehensive documentation please refer to the accompanying ExamForecast.Rmd
and the official Prophet documentation. Below is a minimal example of a forecast for the next month:
library(dplyr)
library(prophet)
exams <- here::here("Data", "per_diem_msk.csv.gz") %>%
readr::read_csv() %>%
filter(modality_code == "CT") %>%
transmute(
ds = as_date(exam_date),
y = n_exams,
)
m <- prophet(exams)
pred <- make_future_dataframe(m, 31, "days", include_history = TRUE)
forecast_exams <- predict(m, pred)
All source code is made available under a MIT or file-specific license. You can freely
use and modify the code, without warranty, so long as you provide attribution
to the authors/cite the article. See LICENSE.md
for the full license text.