This package provides a grammar for data preparation and evaluation of fixed-origin and rolling-origin prediction models using data collected at irregular intervals.
You can install the GitHub version of gpmodels with:
remotes::install_github('ML4LHS/gpmodels')Start by loading and package and defining your time_frame(). A
time_frame is simply a list with the class time_frame and contains
all the key information needed to describe both your fixed dataset (such
as demographics, one row per patient) and your temporal dataset (one row
per observation linked to a timestamp).
library(gpmodels)library(magrittr)
library(lubridate)
future::plan('multisession')
unlink(file.path(tempdir(), 'gpmodels_dir', '*.*'))
tf = time_frame(fixed_data = sample_fixed_data,
temporal_data = sample_temporal_data %>% dplyr::filter(id %in% 1:100),
fixed_id = 'id',
fixed_start = 'admit_time',
fixed_end = 'dc_time',
temporal_id = 'id',
temporal_time = 'time',
temporal_variable = 'variable',
temporal_category = 'category',
temporal_value = 'value',
step = hours(6),
max_length = days(7), # optional parameter to limit to first 7 days of hospitalization
output_folder = file.path(tempdir(), 'gpmodels_dir'),
create_folder = TRUE)names(tf)
#> [1] "fixed_data" "temporal_data" "fixed_id" "fixed_start" "fixed_end" "temporal_id"
#> [7] "temporal_time" "temporal_variable" "temporal_value" "temporal_category" "step" "max_length"
#> [13] "step_units" "output_folder" "fixed_data_dict" "temporal_data_dict" "chunk_size"
tf$step
#> [1] 6
tf$step_units
#> [1] "hour"
tf$fixed_data_dict
#> variable class
#> 1 id integer
#> 2 sex character
#> 3 age numeric
#> 4 race character
#> 5 baseline_cr numeric
#> 6 admit_time POSIXct
#> 7 dc_time POSIXct
tf$temporal_data_dict
#> variable class
#> 1 cr numeric
#> 2 cr_abnl character
#> 3 cr_high character
#> 4 med charactertf = tf %>%
pre_dummy_code()This affects only the temporal data and not the fixed data.
tf$fixed_data_dict
#> variable class
#> 1 id integer
#> 2 sex character
#> 3 age numeric
#> 4 race character
#> 5 baseline_cr numeric
#> 6 admit_time POSIXct
#> 7 dc_time POSIXct
tf$temporal_data_dict
#> variable class
#> 1 cr numeric
#> 2 cr_abnl_high numeric
#> 3 cr_abnl_low numeric
#> 4 cr_abnl_normal numeric
#> 5 cr_high_no numeric
#> 6 cr_high_yes numeric
#> 7 med_acetaminophen numeric
#> 8 med_aspirin numeric
#> 9 med_diphenhydramine numericThe default method writes output to the folder defined in your
time_frame. When you write your output to file, you are allowed to
chain together add_predictors() and add_outcomes() functions. This
is possble because these functions invisibly return a time_frame.
If, however, you set output_file to FALSE, then your actual output
is returned (rather than the time_frame) so you cannot chain
functions.
tf %>%
add_rolling_predictors(variables = 'cr', # Note: You can supply a vector of variables
lookback = hours(12),
window = hours(6),
stats = c(mean = mean,
min = min,
max = max,
median = median,
length = length)) %>%
add_baseline_predictors(variables = 'cr', # add baseline creatinine
lookback = days(90),
offset = hours(10),
stats = c(min = min)) %>%
add_growing_predictors(variables = 'cr', # cumulative max creatinine since admission
stats = c(max = max)) %>%
add_rolling_predictors(category = 'med', # Note: category is always a regular expression
lookback = days(7),
stats = c(sum = sum)) %>%
add_rolling_outcomes(variables = 'cr',
lookahead = hours(24),
stats = c(max = max))
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 1540
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/rolling_predictors_variables_cr_2021_07_12_01_33_50.csv
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 100
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/baseline_predictors_variables_cr_2021_07_12_01_33_52.csv
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 1540
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/growing_predictors_variables_cr_2021_07_12_01_34_17.csv
#> Joining, by = "id"
#> Processing category: med...
#> Allocating memory...
#> Number of rows in final output: 1540
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/rolling_predictors_category_med_2021_07_12_01_34_48.csv
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 1540
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/rolling_outcomes_variables_cr_2021_07_12_01_35_19.csvYou can provide combine_output() with a set of data frames separated
by commas. Or, you can provide a vector of file names using the files
argument. If you leave files blank, it will automatically find all the
.csv files from the output_folder of your time_frame.
This resulting frame is essentially ready for modeling (using
tidymodels, for example). Make sure to keep individual patients in the
same fold if you divide this dataset into multiple folds.
model_data = combine_output(tf)
#> Reading file: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/baseline_predictors_variables_cr_2021_07_12_01_33_52.csv...
#> Reading file: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/growing_predictors_variables_cr_2021_07_12_01_34_17.csv...
#> Reading file: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/rolling_outcomes_variables_cr_2021_07_12_01_35_19.csv...
#> Reading file: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/rolling_predictors_category_med_2021_07_12_01_34_48.csv...
#> Reading file: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/rolling_predictors_variables_cr_2021_07_12_01_33_50.csv...
#> Joining, by = "id"
#> Joining, by = "id"
#> Joining, by = c("id", "time")
#> Joining, by = c("id", "time")
#> Joining, by = c("id", "time")
head(model_data)
#> id sex age race baseline_cr admit_time dc_time baseline_cr_min_2160 time growing_cr_max
#> 1 1 male 66.15955 asian 1.001175 2019-06-02 00:49:23 2019-06-08 10:38:23 NA 0 NA
#> 2 1 male 66.15955 asian 1.001175 2019-06-02 00:49:23 2019-06-08 10:38:23 NA 6 NA
#> 3 1 male 66.15955 asian 1.001175 2019-06-02 00:49:23 2019-06-08 10:38:23 NA 12 1.039322
#> 4 1 male 66.15955 asian 1.001175 2019-06-02 00:49:23 2019-06-08 10:38:23 NA 18 1.217020
#> 5 1 male 66.15955 asian 1.001175 2019-06-02 00:49:23 2019-06-08 10:38:23 NA 24 1.217020
#> 6 1 male 66.15955 asian 1.001175 2019-06-02 00:49:23 2019-06-08 10:38:23 NA 30 1.217020
#> outcome_cr_max_24 med_acetaminophen_sum_168 med_aspirin_sum_168 med_diphenhydramine_sum_168 cr_length_06 cr_length_12 cr_max_06
#> 1 1.217020 0 0 0 1 1 1.003659
#> 2 1.217020 0 0 0 0 1 1.003659
#> 3 1.217020 1 0 0 1 0 1.039322
#> 4 1.179722 1 0 0 2 1 1.217020
#> 5 1.274939 1 0 0 1 2 1.179722
#> 6 1.274939 1 0 0 3 1 1.165989
#> cr_max_12 cr_mean_06 cr_mean_12 cr_median_06 cr_median_12 cr_min_06 cr_min_12
#> 1 1.030098 1.003659 1.030098 1.003659 1.030098 1.0036587 1.030098
#> 2 1.003659 1.003659 1.003659 1.003659 1.003659 1.0036587 1.003659
#> 3 NA 1.039322 NA 1.039322 NA 1.0393216 NA
#> 4 1.039322 1.109985 1.039322 1.109985 1.039322 1.0029506 1.039322
#> 5 1.217020 1.179722 1.109985 1.179722 1.109985 1.1797219 1.002951
#> 6 1.179722 1.069630 1.179722 1.096827 1.179722 0.9460735 1.179722If you want to simply test time_frame, you may prefer not to write
your output to file. You can accomplish this by setting output_file to
FALSE.
tf %>%
add_rolling_predictors(variables = 'cr',
lookback = hours(12),
window = hours(6),
stats = c(mean = mean,
min = min,
max = max,
median = median,
length = length),
output_file = FALSE) %>%
head()
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 1540
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> id time cr_length_06 cr_length_12 cr_max_06 cr_max_12 cr_mean_06 cr_mean_12 cr_median_06 cr_median_12 cr_min_06 cr_min_12
#> 1 1 0 1 1 1.003659 1.030098 1.003659 1.030098 1.003659 1.030098 1.0036587 1.030098
#> 2 1 6 0 1 1.003659 1.003659 1.003659 1.003659 1.003659 1.003659 1.0036587 1.003659
#> 3 1 12 1 0 1.039322 NA 1.039322 NA 1.039322 NA 1.0393216 NA
#> 4 1 18 2 1 1.217020 1.039322 1.109985 1.039322 1.109985 1.039322 1.0029506 1.039322
#> 5 1 24 1 2 1.179722 1.217020 1.179722 1.109985 1.179722 1.109985 1.1797219 1.002951
#> 6 1 30 3 1 1.165989 1.179722 1.069630 1.179722 1.096827 1.179722 0.9460735 1.179722tf %>%
add_rolling_predictors(variables = c('cr', 'med_aspirin'),
lookback = weeks(1),
stats = c(length = length),
output_file = FALSE) %>%
head()
#> Joining, by = "id"
#> Processing variables: cr, med_aspirin...
#> Allocating memory...
#> Number of rows in final output: 1540
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> id time cr_length_168 med_aspirin_length_168
#> 1 1 0 2 0
#> 2 1 6 2 0
#> 3 1 12 3 0
#> 4 1 18 5 0
#> 5 1 24 6 0
#> 6 1 30 9 0tf %>%
add_rolling_predictors(category = 'lab|med',
lookback = hours(12),
stats = c(length = length),
output_file = FALSE) %>%
head()
#> Joining, by = "id"
#> Processing category: lab|med...
#> Allocating memory...
#> Number of rows in final output: 1540
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> id time cr_length_12 med_acetaminophen_length_12 med_aspirin_length_12 med_diphenhydramine_length_12
#> 1 1 0 2 0 0 0
#> 2 1 6 1 0 0 0
#> 3 1 12 1 1 0 0
#> 4 1 18 3 1 0 0
#> 5 1 24 3 0 0 0
#> 6 1 30 4 0 0 0benchmark_results = list()
# future::plan('multisession')
benchmark_results[['multisession']] =
microbenchmark::microbenchmark(
tf %>%
add_rolling_predictors(variable = 'cr',
lookback = hours(48),
window = hours(6),
stats = c(mean = mean,
min = min,
max = max,
median = median,
length = length)),
times = 1
)tf_with_chunks = tf
tf_with_chunks$chunk_size = 20
benchmark_results[['multisession with chunk_size 20']] =
microbenchmark::microbenchmark(
tf_with_chunks %>%
add_rolling_predictors(variable = 'cr',
lookback = hours(48),
window = hours(6),
stats = c(mean = mean,
min = min,
max = max,
median = median,
length = length)),
times = 1
)
#> Processing chunk # 1 out of 5...
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 270
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/chunk_1_rolling_predictors_variables_cr_2021_07_12_01_37_39.csv
#> Processing chunk # 2 out of 5...
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 294
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/chunk_2_rolling_predictors_variables_cr_2021_07_12_01_37_46.csv
#> Processing chunk # 3 out of 5...
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 309
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/chunk_3_rolling_predictors_variables_cr_2021_07_12_01_37_53.csv
#> Processing chunk # 4 out of 5...
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 345
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/chunk_4_rolling_predictors_variables_cr_2021_07_12_01_38_00.csv
#> Processing chunk # 5 out of 5...
#> Joining, by = "id"
#> Processing variables: cr...
#> Allocating memory...
#> Number of rows in final output: 322
#> Parallel processing is ENABLED.
#> Determining missing values for each statistic...
#> Beginning calculation...
#> Completed calculation.
#> The output file was written to: C:\Users\KARAN_~1\AppData\Local\Temp\Rtmp6PiqU2/gpmodels_dir/chunk_5_rolling_predictors_variables_cr_2021_07_12_01_38_07.csvfuture::plan('sequential')
benchmark_results[['sequential']] =
microbenchmark::microbenchmark(
tf %>%
add_rolling_predictors(variable = 'cr',
lookback = hours(48),
window = hours(6),
stats = c(mean = mean,
min = min,
max = max,
median = median,
length = length)),
times = 1
)benchmark_results
#> $multisession
#> Unit: seconds
#> expr
#> tf %>% add_rolling_predictors(variable = "cr", lookback = hours(48), window = hours(6), stats = c(mean = mean, min = min, max = max, median = median, length = length))
#> min lq mean median uq max neval
#> 32.66706 32.66706 32.66706 32.66706 32.66706 32.66706 1
#>
#> $`multisession with chunk_size 20`
#> Unit: seconds
#> expr
#> tf_with_chunks %>% add_rolling_predictors(variable = "cr", lookback = hours(48), window = hours(6), stats = c(mean = mean, min = min, max = max, median = median, length = length))
#> min lq mean median uq max neval
#> 33.79288 33.79288 33.79288 33.79288 33.79288 33.79288 1
#>
#> $sequential
#> Unit: seconds
#> expr
#> tf %>% add_rolling_predictors(variable = "cr", lookback = hours(48), window = hours(6), stats = c(mean = mean, min = min, max = max, median = median, length = length))
#> min lq mean median uq max neval
#> 127.0335 127.0335 127.0335 127.0335 127.0335 127.0335 1