ResPredAI 1.6.2

Antimicrobial Resistance predictions via AI models

ResPredAI

Antimicrobial resistance predictions via artificial intelligence models

Implementation of the pipeline described in:

Bonazzetti, C., Rocchi, E., Toschi, A. et al. Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections. npj Digit. Med. 8, 319 (2025). https://doi.org/10.1038/s41746-025-01696-x

Website | Documentation | Installation | Quick Start | CLI Commands | Citation

A reproducible machine learning framework designed to accelerate clinical decision-making by predicting antimicrobial resistance patterns from patient data.

Links

• Project Website - Overview of the project, original paper, and related work
• Documentation - Installation guides, CLI reference, tutorials, and API documentation

Installation

Install from PyPI:

pip install respredai

Or install from source:

git clone https://github.com/EttoreRocchi/ResPredAI.git
cd ResPredAI
# For development (includes pytest)
pip install -e ".[dev]"

Testing the Installation

Verify the installation:

respredai --version

Quick Start

1. Create a configuration file

respredai create-config my_config.ini

2. Edit the configuration file

Edit my_config.ini with your data paths and parameters:

[Data]
data_path = ./data/my_data.csv
targets = Target1,Target2
continuous_features = Feature1,Feature2,Feature3
# group_column = PatientID  # Optional: prevents data leakage when you have multiple samples per patient

[Pipeline]
models = LR,RF,XGB,CatBoost
outer_folds = 5
inner_folds = 3
# Repeated CV: set >1 for more robust estimates
outer_cv_repeats = 1
# Probability calibration: post-hoc calibration on best estimator
calibrate_probabilities = false
probability_calibration_method = sigmoid  # sigmoid or isotonic
probability_calibration_cv = 5
# Threshold optimization
calibrate_threshold = false
threshold_method = auto
# Threshold optimization objective: youden (default), f1, f2, cost_sensitive
threshold_objective = youden
# Cost weights for cost_sensitive objective (VME = false susceptible, ME = false resistant)
vme_cost = 1.0
me_cost = 1.0

[Uncertainty]
# Margin around threshold for flagging uncertain predictions (0-0.5)
margin = 0.1

[Reproducibility]
seed = 42

[Log]
verbosity = 1
log_basename = respredai.log

[Resources]
n_jobs = -1

[ModelSaving]
enable = true
compression = 3

[Imputation]
method = none
strategy = mean
n_neighbors = 5
estimator = bayesian_ridge

[Output]
out_folder = ./output/

3. Run the pipeline

respredai run --config my_config.ini

CLI Commands

Run the pipeline

respredai run --config path/to/config.ini [--quiet]

Train models using nested cross-validation with the specified configuration.

📖 Detailed Documentation - Complete guide with all configuration options and workflow details.

Train models for cross-dataset validation

respredai train --config path/to/config.ini [--models LR,RF] [--output ./trained/]

Train models on the entire dataset using GridSearchCV for hyperparameter tuning. Saves one model file per model-target combination for later use with evaluate.

📖 Detailed Documentation - Complete guide with output structure and workflow.

Evaluate on new data

respredai evaluate --models-dir ./output/trained_models --data new_data.csv --output ./eval/

Apply trained models to new data with ground truth. Outputs predictions and metrics.

📖 Detailed Documentation - Complete guide with data requirements and output format.

Extract feature importance

respredai feature-importance --output <output_folder> --model <model_name> --target <target_name> [--top-n 20]

Extract and visualize feature importance/coefficients from trained models across all outer cross-validation iterations. Uses SHAP as fallback for models without native feature importance.

📖 Detailed Documentation - Complete guide with interpretation, examples, and statistical considerations.

List available models

respredai list-models

Display all available machine learning models with descriptions.

Available Models:
┌────────────┬──────────────────────────┐
│ Code       │ Name                     │
├────────────┼──────────────────────────┤
│ LR         │ Logistic Regression      │
│ MLP        │ Neural Network           │
│ XGB        │ XGBoost                  │
│ RF         │ Random Forest            │
│ CatBoost   │ CatBoost                 │
│ TabPFN     │ TabPFN                   │
│ RBF_SVC    │ RBF SVM                  │
│ Linear_SVC │ Linear SVM               │
│ KNN        │ K-Nearest Neighbors      │
└────────────┴──────────────────────────┘

Create a template configuration file

respredai create-config output_path.ini

Generate a template configuration file that you can edit for your data.

📖 Detailed Documentation - Complete guide to configuration file structure and customization.

Validate a configuration file

respredai validate-config <path_to_config.ini> [--check-data]

Validate a configuration file without running the pipeline. It can also check that the dataset load without errors.

📖 Detailed Documentation - Complete guide to configuration file validation.

Show information

respredai info

Display information about ResPredAI including scientific paper citation and version details.

Or just:

respredai --version

to show the installed version of ResPredAI.

Output

The pipeline generates:

• Confusion matrices: PNG files with heatmaps showing model performance for each target
• Detailed metrics tables: CSV files with comprehensive metrics (precision, recall, F1, MCC, balanced accuracy, AUROC, VME, ME, Brier Score, ECE, MCE) with mean, std, and 95% CI
• Calibration diagnostics: Reliability curves (calibration plots) per fold and aggregate
• Trained models: Saved models for resumption and feature importance extraction (if model saving enabled)
• Feature importance: Plots and CSV files showing feature importance/coefficients (generated separately)
• Log files: Detailed execution logs (if verbosity > 0)

Output Structure

output_folder/
├── models/                                         # Trained models (if model saving enabled)
│   └── {Model}_{Target}_models.joblib
├── trained_models/                                 # Models for cross-dataset validation (from train command)
│   ├── {Model}_{Target}.joblib
│   └── training_metadata.json
├── metrics/                                        # Detailed performance metrics
│   ├── {target_name}/
│   │   ├── {model_name}_metrics_detailed.csv      # Includes Brier Score, ECE, MCE
│   │   └── summary.csv                            # Summary across all models
│   └── summary_all.csv                            # Global summary
├── calibration/                                    # Calibration diagnostics
│   └── reliability_curve_{model}_{target}.png     # Reliability curves per fold + aggregate
├── feature_importance/                             # Feature importance (if extracted)
│   └── {target_name}/
│       ├── {model_name}_feature_importance.csv    # Importance values
│       └── {model_name}_feature_importance.png    # Barplot visualization
├── confusion_matrices/                             # Confusion matrix heatmaps
│   └── Confusion_matrix_{model_name}_{target_name}.png
├── report.html                                     # Comprehensive HTML report (includes calibration section)
└── respredai.log                                   # Execution log (if verbosity > 0)

Changelog

See the full history of changes in the CHANGELOG.md file.

Citation

If you use ResPredAI in your research, please cite:

@article{Bonazzetti2025,
  author = {Bonazzetti, Cecilia and Rocchi, Ettore and Toschi, Alice and Derus, Nicolas Riccardo and Sala, Claudia and Pascale, Renato and Rinaldi, Matteo and Campoli, Caterina and Pasquini, Zeno Adrien Igor and Tazza, Beatrice and Amicucci, Armando and Gatti, Milo and Ambretti, Simone and Viale, Pierluigi and Castellani, Gastone and Giannella, Maddalena},
  title = {Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections},
  journal = {npj Digital Medicine},
  volume = {8},
  pages = {319},
  year = {2025},
  doi = {10.1038/s41746-025-01696-x},
  url = {https://doi.org/10.1038/s41746-025-01696-x}
}

Funding

This research was supported by EU funding within the NextGenerationEU-MUR PNRR Extended Partnership initiative on Emerging Infectious Diseases (Project no. PE00000007, INF-ACT).

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines on setting up a development environment, running tests, and submitting pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.