Summary of Malade: Orchestration Of Llm-powered Agents with Retrieval Augmented Generation For Pharmacovigilance, by Jihye Choi et al.
MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance
by Jihye Choi, Nils Palumbo, Prasad Chalasani, Matthew M. Engelhard, Somesh Jha, Anivarya Kumar, David Page
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Quantitative Methods (q-bio.QM)
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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 paper proposes a novel approach to trustworthy medical knowledge synthesis, extraction, and summarization using Large Language Models (LLMs). Specifically, it focuses on Pharmacovigilance (PhV), which involves identifying Adverse Drug Events (ADEs) from diverse text sources. The authors present MALADE, a collaborative multi-agent system powered by LLM with Retrieval Augmented Generation for ADE extraction from drug label data. This technique combines query augmentation and response generation to extract drug-outcome associations in a structured format along with their strengths. Instantiated with GPT-4 Turbo or GPT-4o, MALADE demonstrates its efficacy with an Area Under ROC Curve of 0.90 against the OMOP Ground Truth table of ADEs. The authors leverage the Langroid multi-agent LLM framework and provide their implementation on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computers called Large Language Models to help find important information in medical texts. They want to make it easier to identify problems that can happen when taking medicines, like bad side effects. The researchers created a system called MALADE that uses these computers to look at text from different sources and find patterns between medicines and the problems they might cause. They tested their system and found it was very good at finding these patterns. This could help doctors and scientists better understand how medicines work and make better decisions about what treatments are safe. |
Keywords
» Artificial intelligence » Gpt » Retrieval augmented generation » Roc curve » Summarization
