Summary of Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-aware Sql, by Jeffery L. Painter et al.
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL
by Jeffery L. Painter, Venkateswara Rao Chalamalasetti, Raymond Kassekert, Andrew Bate
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach employs Large Language Models (LLMs) to transform natural language queries (NLQs) into Structured Query Language (SQL) queries for efficient and accurate information retrieval from pharmacovigilance (PV) databases. The model utilizes a business context document, aiming to improve the efficiency and accuracy of PV database query outputs. This innovation has the potential to significantly enhance the speed and reliability of PV data querying, ultimately benefiting the development of safer and more effective medications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are working on making it easier and faster to search through huge databases that contain information about medicines and side effects. They’re using special computer models called Large Language Models (LLMs) to help turn words into a language that computers can understand, like SQL. This will make it quicker and more accurate for people to find the information they need. It’s an important step in making sure new medicines are safe and work well. |
