Explainable AI for PTSD Biotypes

Project Overview

This project utilizes Explainable Artificial Intelligence (XAI) techniques to identify distinct biotypes within Post-Traumatic Stress Disorder (PTSD). By making complex AI models interpretable, we aim to understand the specific neural and behavioral features that characterize different PTSD subgroups, paving the way for more personalized and effective treatment strategies.

Key Research Questions

• How can XAI methods reveal the key features driving PTSD biotype classifications?
• What are the most reliable neuroimaging and clinical markers distinguishing PTSD subtypes?
• Can interpretable AI models provide insights into the underlying mechanisms of different biotypes?
• How can XAI-derived biotypes guide personalized treatment selection for PTSD patients?

Methodology

We apply various XAI methods (e.g., SHAP, LIME, attention mechanisms) to deep learning and other machine learning models trained on multimodal data (fMRI, DTI, clinical surveys) from individuals with PTSD. This allows us to interpret model predictions and identify the features most critical for distinguishing biotypes.

Current Status

We are developing and applying XAI techniques to existing PTSD datasets. Initial results are demonstrating the utility of these methods in uncovering meaningful patterns and generating hypotheses about the heterogeneity within PTSD.