AutoScore_fine_tuning {AutoScore} | R Documentation |
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
)
Arguments
train_set |
A processed |
validation_set |
A processed |
final_variables |
A vector containing the list of selected variables, selected from Step(ii) |
cut_vec |
Generated from STEP(iii) |
max_score |
Maximum total score (Default: 100). |
metrics_ci |
whether to calculate confidence interval for the metrics of sensitivity, specificity, etc. |
Value
Generated final table of scoring model for downstream testing
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_weighting
, AutoScore_testing
,Run vignette("Guide_book", package = "AutoScore")
to see the guidebook or vignette.
Examples
## Please see the guidebook or vignettes