Challenge
Pharma and life-sciences teams sit on mountains of unstructured text — clinical study reports, adverse event narratives, lab notes, regulatory submissions, and investigator emails. Important entities (drug names, dosages, patient demographics, adverse events, lab values) are scattered, noisy, abbreviated, and written in diverse templates and languages. Manual review is slow, inconsistent, and costly; downstream analytics, safety signal detection, and regulatory reporting suffer from incomplete or non-standardized data.
Our Solution
We designed a production NER data-extraction pipeline tailored for pharma and life-sciences that converts unstructured documents into normalized, high-quality entity records. The approach blends domain-adapted transformer models, rule-based post-processing, human-in-the-loop validation and an auditable retraining loop — delivering both accuracy and regulatory traceability.
Features
- Domain-tuned NER models
- Confidence & provenance
Benefits
- Transformed Unstructured Text into Structured, Usable Data
- Speeds Up Information Discovery
- Enhanced Data Quality and Consistency
- Drives Faster Decision-Making
- Supports Human-in-the-Loop Collaboration
Tech Stack
Python, GPT 4o model