Analyze up to 1.4 M chars per hour for real time processing and up to 3.5 M chars per hour for batch processing.

The model addresses the critical need to identify adverse drug reactions (ADRs) - unintended and sometimes harmful responses to medications. It's crucial for healthcare professionals and patients to understand the potential side effects for informed care and decision-making. Especially in cases with multiple drug mentions, this model proficiently correlates drugs with their respective adverse reactions, discerning if an event is drug-induced.

In the digital era, patients frequently share medication experiences across various platforms like online reviews, social media, and forums. This model meticulously scans such diverse content, including clinical notes, to detect drug-associated adverse reactions. It serves as a proactive tool for early ADR identification, thereby enhancing patient safety and refining drug prescription practices.

Covered entities: DRUG, ADE. Covered assertion statuses: absent, present, conditional, associated_with_someone_else, hypothetical, possible. Relations: 0 (absent), 1 (present). Covered classes: ADE, noADE