AutoScore_fine_tuning: AutoScore STEP(iv): Fine-tune the score by revising cut_vec with domain knowledge (AutoScore Module 5)

Description

Domain knowledge is essential in guiding risk model development. For continuous variables, the variable transformation is a data-driven process (based on "quantile" or "kmeans" ). In this step, the automatically generated cutoff values for each continuous variable can be fine-tuned by combining, rounding, and adjusting according to the standard clinical norm. Revised cut_vec will be input with domain knowledge to update scoring table. User can choose any cut-off values/any number of categories. Then final Scoring table will be generated. Run vignette("Guide_book", package = "AutoScore") to see the guidebook or vignette.

Usage

AutoScore_fine_tuning(
  train_set,
  validation_set,
  final_variables,
  cut_vec,
  max_score = 100,
  metrics_ci = FALSE
)

Value

Generated final table of scoring model for downstream testing

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.
cut_vec: Generated from STEP(iii) AutoScore_weighting.Please follow the guidebook
max_score: Maximum total score (Default: 100).
metrics_ci: whether to calculate confidence interval for the metrics of sensitivity, specificity, etc.

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

Examples

Run this code

## Please see the guidebook or vignettes