Data is available only upon formal request and subject to approval.
Approved users receive a secure institute account and work with the data exclusively in our Trusted Research Environment (TRE) via remote desktop.
Request data (Email to us)Clinical study data are often first captured in spreadsheets (Excel/CSV). While convenient, flexible column naming frequently leads to ambiguous or inconsistent variable names and labels. These issues may only become apparent during analysis, causing delays, repeated clarification between clinicians and data scientists, and limited reuse and harmonization across studies.
As a team effort (Eren Tasken, Berkay Uras, Lakshmi Sowjanya Batchu, Marcel Müller), we developed a lightweight, extensible, human-in-the-loop prototype for AI-assisted standardization of clinical research metadata at the variable level. The system proposes improved variable names and labels (demonstrated on the PEACHES and BEARR studies) and can optionally suggest semantic annotations. All recommendations remain under human control: users review, edit, and accept/reject suggestions to produce a finalized mapping. The workflow operates on REDCap-style metadata and lightweight summaries and avoids any modification of live REDCap projects.
The prototype follows a modular retrieval-augmented generation (RAG) architecture with a FastAPI backend and a Next.js frontend. Users upload a CSV data dictionary; the system generates ranked normalization suggestions using an API-based LLM (initially Grok-4-1-fast-non-reasoning) to enable rapid prototyping without local model infrastructure.
In an initial evaluation on 28 variables, the system produced human-readable naming suggestions for 10/28 (36.0%) when only the variable name was provided. When variable label context was included, performance improved to 22/28 (78.6%), highlighting the importance of informative labels.
We are submitting this work to the 4th Heidelberg Spring Symposium on Medical Informatics (May 13, 2026). The prototype source code and documentation will be shared via TRACE (Trusted Research Access & Collaboration Environment) for download.
Check out the work on Github: GitHub - erentasken/FieldForge
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| Version | Language | Type | Relation | Author | Date |
|---|---|---|---|---|---|
| Global v1 (Python v1) selected | Python | Multi-file Archive | Initial Implementation | eren.tasken | 2026-01-30 |