AutoScore_fine_tuning_Ordinal: AutoScore STEP(iv) for ordinal outcomes: Fine-tune the score...

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

AutoScore STEP(iv) for ordinal outcomes: 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_Ordinal(
  train_set,
  validation_set,
  final_variables,
  link = "logit",
  cut_vec,
  max_score = 100,
  n_boot = 100,
  report_cindex = 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_Ordinal.
link	The link function used to model ordinal outcomes. Default is "logit" for proportional odds model. Other options are "cloglog" (proportional hazards model) and "probit".
cut_vec	Generated from STEP(iii) AutoScore_weighting_Ordinal.
max_score	Maximum total score (Default: 100).
n_boot	Number of bootstrap cycles to compute 95% CI for performance metrics.
report_cindex	Whether to report generalized c-index for model evaluation (Default:FALSE for faster evaluation).

Value

Generated final table of scoring model for downstream testing

References

• Saffari SE, Ning Y, Feng X, Chakraborty B, Volovici V, Vaughan R, Ong ME, Liu N, AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes, arXiv:2202.08407

See Also

AutoScore_rank_Ordinal, AutoScore_parsimony_Ordinal, AutoScore_weighting_Ordinal, AutoScore_testing_Ordinal.

Examples

## Please see the guidebook or vignettes

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