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A pre-clinical Edge-AI SDK for diabetes management validation.

Project description

IINTS-AF SDK

PyPI version Open In Colab Python Package CI

Intelligent Insulin Titration System for Artificial Pancreas

IINTS-AF is a safety-first simulation and validation platform for insulin dosing algorithms. It lets you test AI or classical controllers on virtual patients, enforce deterministic safety constraints, and generate audit-ready clinical reports before anything touches a real patient.

In one session you can:

  • Run a clinic-safe preset and compare against PID and standard pump baselines
  • Import real-world CGM CSV into a standard schema + scenario JSON
  • Use the bundled demo CGM data pack (zero setup)
  • Export a clean PDF report plus full audit trail (JSONL/CSV)
  • Stress-test sensor noise, pump limits, and human-in-the-loop interventions
  • Generate patient profiles with ISF/ICR + dawn phenomenon

Who it’s for:

  • Diabetes researchers and clinicians validating new control strategies
  • ML engineers benchmarking AI controllers with medical safety rails
  • Developers building decision-support systems for closed-loop insulin delivery

Installation

Install the SDK directly via PyPI:

pip install iints-sdk-python35

Quick Start (CLI)

iints quickstart --project-name iints_quickstart
cd iints_quickstart
iints presets run --name baseline_t1d --algo algorithms/example_algorithm.py

One-line full run (CSV + audit + PDF + baseline):

iints run-full --algo algorithms/example_algorithm.py \
  --scenario-path scenarios/clinic_safe_baseline.json \
  --output-dir results/run_full

Import real-world CGM data:

iints import-data --input-csv data/my_cgm.csv --output-dir results/imported

Try the bundled demo data pack:

iints import-demo --output-dir results/demo_import

Official real-world datasets (download on demand):

iints data list
iints data info aide_t1d
iints data fetch aide_t1d

Some datasets require approval and are marked as request in the registry. iints data info prints BibTeX + citation text for easy referencing.

Offline sample dataset (no download required):

iints data fetch sample --output-dir data_packs/sample

Nightscout import (optional dependency):

pip install iints-sdk-python35[nightscout]
iints import-nightscout --url https://your-nightscout.example --output-dir results/nightscout_import

Scenario generator:

iints scenarios generate --name "Random Stress Test" --output-path scenarios/generated_scenario.json

Parallel batch runs:

iints run-parallel --algo algorithms/example_algorithm.py --scenarios-dir scenarios --output-dir results/batch

Or run the full demo workflow (import + run + report) in one script:

python3 examples/demo_quickstart_flow.py

Quick Start (Python)

import iints
from iints.core.algorithms.pid_controller import PIDController

outputs = iints.run_simulation(
    algorithm=PIDController(),
    scenario="scenarios/example_scenario.json",
    patient_config="default_patient",
    duration_minutes=720,
    seed=42,
    output_dir="results/quick_run",
)

Notebook Guide

Hands-on Jupyter notebooks live in examples/notebooks/

  • Quickstart end-to-end run
  • Presets + scenario validation
  • Safety supervisor behavior
  • Audit trail + PDF report export
  • Baseline comparison + clinical metrics
  • Sensor/pump models + human-in-the-loop
  • Optional Torch/LSTM usage
  • Ablation study (with/without Supervisor)

Documentation

  • Product manual: SDK_COMPREHENSIVE_GUIDE.md
  • Notebook index: examples/notebooks/README.md
  • Technical README: TECHNICAL_README.md
  • API Stability: API_STABILITY.md

Related Work & Inspiration

We borrow techniques from the broader CGM/APS ecosystem, while differentiating with a safety‑first, audit‑ready workflow:

Ethics & Safety

This SDK is for research and validation. It is not a medical device and does not provide clinical dosing advice.

“Code shouldn’t be a secret when it’s managing a life.” — Bobbaers Rune

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