IAML — Integrated AutoML for Medical Labs

IAML (Integrated AutoML for Medical Labs) is a Python framework developed by IIAS to make machine learning more accessible to clinical research teams. It brings preprocessing, model search and evaluation into one workflow for classification, regression and survival analysis on tabular data.

Researchers can inspect the steps of a selected pipeline, evaluate its predictions and generate explanations to discuss with clinicians and data scientists. IAML includes a broad set of built-in methods. Go further with customization. Add your team's preprocessing steps, models, metrics, validation splitters and search optimizers to adapt the workflow to your research domain. These contributions can be shared and reused across studies. The extension guide shows how to get started.

Clinical research workflow

• Build prediction pipelines: search preprocessing steps, models and their parameters through a Python API.
• Evaluate a study outcome: choose the metric and validation strategy, then assess the selected candidate on held-out data.
• Inspect and explain: describe pipeline steps, compute SHAP explanations and generate task-specific performance plots.
• Document an experiment: collect method references and optionally retain cross-validation records with keep_training_history=True.

The research guide shows how to use these outputs when reporting a study and recording the settings needed to repeat an experiment.

Installation

Use Python 3.10 or later:

python -m pip install PyIAML

The distribution is named PyIAML. The Python import is iaml.

How to run

Save this example as example.py and run it with python example.py. It uses a dataset bundled with scikit-learn, so no dataset download is needed.

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from iaml import IAML

if __name__ == "__main__":
    X, y = load_breast_cancer(return_X_y=True, as_frame=True)
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, stratify=y, random_state=42
    )
    search = IAML(max_duration=30, max_workers=1)
    search.fit(X_train, y_train)
    chosen_model = search.chosen_candidate
    print(chosen_model.evaluate(X_test, y_test))

chosen_model is the selected model, including its preprocessing. evaluate scores it on the held-out test set. The dataset labels are 0 for malignant and 1 for benign. Keep the __main__ guard because training uses multiprocessing. The example uses one worker and a 30-second search budget. Final fitting can take additional time.

Documentation

The user guides cover data preparation, model search, evaluation and interpretation:

• Quick Start: install IAML and run an example.
• 01 / Build: prepare data and configure a search.
• 02 / Evaluate: assess predictions on held-out data.
• 03 / Explain: inspect methods and interpret feature contributions.
• Study reporting: save outputs and record experiment settings.
• Extending IAML: add reusable methods for your team's research.
• Component availability: explore the main component families and their API documentation.

Credits

• Rudy MERIEUX
• Robin BOURACHOT
• Hugo RUELLET
• Youssouf DAHLOUK