Explainable NLP for ADHD Anamnesis

Overview

Building an explainable NLP system for German primary school reports to support retrospective ADHD assessment in collaboration with clinicians.

Role and scope

• Full-stack ownership: Python backend (models, REST API) and React/Node.js frontend.
• Designed evidence highlighting and clinical metrics (AUC/ROC, sensitivity, specificity) for decision support.

Technical highlights

• Compared encoder and decoder models plus LLM APIs for quality, explainability, and cost.
• Implemented RAG-style evidence retrieval and token-level attribution for UI highlighting.
• Continuous evaluation with clinician feedback loops; targeting a clinical pilot and publication.

Status

• Ongoing research and development with iterative model and UI improvements.