🩺 OpenSafety-1.5B: Clinical Pharmacovigilance & MedDRA Coding Foundation Model

OpenSafety-1.5B is a specialized clinical language model trained for end-to-end Pharmacovigilance (PV). It extracts suspect drugs, identifies Adverse Drug Reactions (ADRs) from unstructured clinical text, maps symptoms to MedDRA v28.0 Preferred Terms (PT) and System Organ Classes (SOC), and performs regulatory Listedness (Expectedness) Assessment against Reference Safety Information (RSI).

This model powers the official opensafety Python package.

Model Details

• Developed by: OpenSafety Team (thesumith)
• Model Type: Causal Language Model (Fine-tuned Transformer)
• Parameters: 1.5 Billion
• Language: English
• License: Apache 2.0
• Supported Standards: MedDRA v28.0, ICH E2B(R3)
• Python Library: opensafety on PyPI

Key Capabilities

1. Suspect Drug Identification: Identifies the primary suspect drug and dosage regimen from complex clinical narratives.
2. Adverse Event (AE) Extraction: Extracts raw patient verbatim descriptions.
3. MedDRA v28.0 Semantic Coding: Automatically maps patient language to official MedDRA Lowest Level Terms (LLT), Preferred Terms (PT), and Primary System Organ Classes (SOC).
4. Regulatory Listedness Assessment: Cross-references reported events against drug package inserts (FDA USPI, SmPC Section 4.8) to categorize events as LISTED or UNLISTED with regulatory audit justifications.

How to Use

Method 1: Using the Official opensafety Python Library (Recommended)

pip install opensafety