AutoScore_weighting: AutoScore STEP(iii): Generate the initial score with the...

In AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator

AutoScore STEP(iii): Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)

Description

AutoScore STEP(iii): Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)

Usage

AutoScore_weighting(
  train_set,
  validation_set,
  final_variables,
  max_score = 100,
  categorize = "quantile",
  max_cluster = 5,
  quantiles = c(0, 0.05, 0.2, 0.8, 0.95, 1),
  metrics_ci = FALSE
)

Arguments

train_set	A processed data.frame that contains data to be analyzed, for training.
validation_set	A processed data.frame that contains data for validation purpose.
final_variables	A vector containing the list of selected variables, selected from Step(ii)AutoScore_parsimony. Run vignette("Guide_book", package = "AutoScore") to see the guidebook or vignette.
max_score	Maximum total score (Default: 100).
categorize	Methods for categorize continuous variables. Options include "quantile" or "kmeans" (Default: "quantile").
max_cluster	The max number of cluster (Default: 5). Available if categorize = "kmeans".
quantiles	Predefined quantiles to convert continuous variables to categorical ones. (Default: c(0, 0.05, 0.2, 0.8, 0.95, 1)) Available if categorize = "quantile".
metrics_ci	whether to calculate confidence interval for the metrics of sensitivity, specificity, etc.

Value

Generated cut_vec for downstream fine-tuning process STEP(iv) AutoScore_fine_tuning.

References

• Xie F, Chakraborty B, Ong MEH, Goldstein BA, Liu N. AutoScore: A Machine Learning-Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records. JMIR Medical Informatics 2020;8(10):e21798

See Also

AutoScore_rank, AutoScore_parsimony, AutoScore_fine_tuning, AutoScore_testing, Run vignette("Guide_book", package = "AutoScore") to see the guidebook or vignette.

AutoScore documentation built on Oct. 16, 2022, 1:06 a.m.