🚶🏻‍♀️ Triad “parkinson-like” Risk — MVP

Explainable-first AI screening for triad-positive subjects (RBD, hyposmia, depression)
MVP designed for prospective research: risk prediction, surrogate clinical rules, and SHAP interpretability.

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✨ Overview

This project introduces a decision-support system for selecting “triad-positive” subjects (prodromal Parkinson’s disease) for prospective studies.
It is not a medical device, but a research/triage tool, ready to be validated in clinical trials.

• Core algorithm: Balanced Random Forest (ctGAN + Lazy Prediction).
• Explainability: SHAP (global/local), surrogate tree, clinical rules.
• Screening mode: Decision Curve Analysis, PPV/NPV at custom prevalence.
• Output: Streamlit webapp + individual PDF reports.

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🖥️ Webapp (Streamlit MVP)

Patient input	Risk results	PDF export

Key features:

• Single patient or batch CSV input.
• Color-coded risk bands (green/yellow/orange/red).
• Screening mode: set prevalence → PPV/NPV estimate.
• Local SHAP contributions (top-5) for clinical transparency.
• Surrogate rules (interpretable tree) for clinical validation.
• PDF export with minimal QC, thresholds, SHAP, and rules.

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📊 Architecture

• Notebooks (EDA, preprocessing, explainability, robustness) → /notebooks
• Models & scaler → /models
• Surrogate rules (JSON/YAML) → /artifacts
• Streamlit app → /app/app.py
• Results/metrics → /tables and /figurez

Integrated pipeline → from biomechanical analysis to clinical reports.

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🔍 Key Techniques

• Generative data balancing → ctGAN
• Advanced explainability → SHAP, SHAPSet plot, surrogate tree with 95% CI
• Robustness analysis → cross-validation, subgroup analysis, calibration
• Decision support → Decision Curve Analysis (Net Benefit)

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📑 Example of PDF Report

Includes:

• Patient input
• Triad probability + risk band
• Sensitivity/Specificity @ threshold
• PPV/NPV at custom prevalence
• SHAP top-5 contributions
• Surrogate rule matched
• Minimal QC (range, missing, sex, H-Y)

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🚀 Setup & Run

Clone the repo and run the Streamlit app:

git clone https://github.com/DanteTrb/Triad2PD.git
cd Triad2PD/app
pip install -r ../requirements.txt
streamlit run app.py

🧑‍🔬 Clinical Research Disclaimer

•	Research/triage use only.
•	Does not replace clinical judgment.
•	Not a certified medical device.

🏆 Credits & Vision

•	Author: Dante Trabassi, Sapienza University of Rome
•	Focus: Explainable AI for prodromal Parkinson’s disease
•	Goal: bringing transparency and biomechanical rigor to clinical trials.