triageR provides a streamlined, reproducible workflow for building, validating, and reporting clinical prediction models in R. It combines standard machine learning tools with an optional AI agent that recommends appropriate statistical methods, runs automated sensitivity analyses, and flags common clinical modelling pitfalls. Reports are generated in a format aligned with TRIPOD+AI reporting guidance, to support reproducible, guideline-conscious research.
Most R machine learning tooling (tidymodels,
mlr3, caret) is general-purpose. Clinical
researchers are left to manually stitch together model fitting,
validation, sensitivity analysis, explainability, and TRIPOD-compliant
reporting across many separate packages. triageR brings these into one
coherent workflow, with clinically-aware safeguards built in — such as
warning when validation is performed only on training data, and flagging
low events-per-variable ratios before you finalize a model.
The AI agent layer is entirely optional — every core function works without any LLM/API key. The agent adds method recommendations, plain language pipeline review summaries, and assists with sensitivity analysis interpretation, but is never required to fit, validate, or explain a model.
You can install the development version of triageR from GitHub with:
# install.packages("devtools")
devtools::install_github("DevWebWacky/triageR")This example uses the PIMA Indians Diabetes dataset to demonstrate the full triageR pipeline, from raw data to a validated, explainable model.
library(triageR)
library(mlbench)
data(PimaIndiansDiabetes)
pima <- PimaIndiansDiabetes
pima$id <- seq_len(nrow(pima))
# convert physiologically impossible zeros to real missing values
pima$glucose[pima$glucose == 0] <- NA
pima$pressure[pima$pressure == 0] <- NA
pima$triceps[pima$triceps == 0] <- NA
pima$insulin[pima$insulin == 0] <- NA
pima$mass[pima$mass == 0] <- NA
# 1. Load and standardize
pima_loaded <- tr_load_clinical(pima, id_col = "id")
# 2. Check and impute missing data
tr_check_missing(pima_loaded)
#> Missing data summary:
#> - 5 of 10 columns have missing values
#> - 768 total rows
#>
#> # A tibble: 10 × 3
#> column n_missing pct_missing
#> <chr> <int> <dbl>
#> 1 insulin 374 48.7
#> 2 triceps 227 29.6
#> 3 pressure 35 4.6
#> 4 mass 11 1.4
#> 5 glucose 5 0.7
#> 6 pregnant 0 0
#> 7 pedigree 0 0
#> 8 age 0 0
#> 9 diabetes 0 0
#> 10 id 0 0
pima_imputed <- tr_impute(pima_loaded[, setdiff(names(pima_loaded), "id")],
method = "mice", m = 5)# 3. Split, fit, and validate
set.seed(42)
train_idx <- sample(seq_len(nrow(pima_imputed)), size = floor(0.7 * nrow(pima_imputed)))
train <- pima_imputed[train_idx, ]
test <- pima_imputed[-train_idx, ]
model <- tr_fit(train, outcome = "diabetes", engine = "logistic_reg")
#> Model fitted successfully using engine: logistic_reg
validation <- tr_validate(model, newdata = test)
#> Validation metrics (newdata):
#>
#> # A tibble: 4 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc binary 0.835
#> 2 sens binary 0.587
#> 3 spec binary 0.865
#> 4 accuracy binary 0.775
#>
#> Confusion Matrix:
#>
#> Truth
#> Prediction neg pos
#> neg 135 31
#> pos 21 44
# 4. Review the pipeline for common pitfalls
review <- tr_agent_review(train, model, use_agent = FALSE)See vignette("triageR-intro") for the full walkthrough,
including model explainability, sensitivity analysis, engine comparison
(logistic regression vs. random forest vs. boosted trees), and TRIPOD+AI
report generation.
| Layer | Function | Purpose |
|---|---|---|
| Data | tr_load_clinical() |
Load and standardize clinical data |
| Data | tr_check_missing() |
Missingness summary and visualization |
| Data | tr_impute() |
Multiple imputation (mice / missForest) |
| Model | tr_fit() |
Fit a binary clinical prediction model |
| Model | tr_validate() |
Discrimination metrics, confusion matrix, and ROC curve |
| Model | tr_explain() |
Variable importance / SHAP explanations |
| Agent | tr_recommend_method() |
AI-suggested statistical/ML approach |
| Agent | tr_sensitivity() |
Automated sensitivity analysis battery |
| Agent | tr_agent_review() |
Pipeline pitfall checks (imbalance, EPV, leakage) |
| Report | tr_tripod_report() |
TRIPOD+AI-aligned HTML/docx report |
```
triageR’s AI-generated recommendations and summaries are drafting aids intended to support, not replace, expert clinical and statistical judgment. The TRIPOD+AI report generator is a drafting tool and does not guarantee full compliance with the TRIPOD+AI checklist — always review against the official checklist at the EQUATOR Network.
MIT © Uwakmfon Usen Paul