Applied NLP / Academic prototype

From form to structured fields

Comparing extraction approaches on synthetic safety reports.

Python / traditional ML / transformers

01 /

The task

Convert differently organized safety forms into a consistent structured representation. Separate document reading, normalization, field alignment and evaluation so failures can be inspected.

02 /

The experiment

The workflow includes filling synthetic PDF report forms before extraction. It uses 130 synthetic reports: 30 CIOMS and 100 MedWatch forms. Python workflows compare traditional machine learning and transformer-based extraction in an academic, synthetic-data study.

03 /

The boundary

Synthetic-data performance cannot establish readiness for real clinical or regulated use. This is a learning prototype with source-to-field traceability and a clear human-review requirement.

Synthetic input / illustrative only

“A synthetic report describes
Product A and Event B.”

Product field
Product A
Event field
Event B
Review status
Human review required
View the project repository

Continue exploring

MedSafe SafetyDB

A conversation starts here

Good systems begin
with good questions.

For opportunities in health informatics, safety systems, healthcare data or applied software, let’s connect.