Summary of Large Language Models Enabled Multiagent Ensemble Method For Efficient Ehr Data Labeling, by Jingwei Huang et al.
Large language models enabled multiagent ensemble method for efficient EHR data labeling
by Jingwei Huang, Kuroush Nezafati, Ismael Villanueva-Miranda, Zifan Gu, Ann Marie Navar, Tingyi Wanyan, Qin Zhou, Bo Yao, Ruichen Rong, Xiaowei Zhan, Guanghua Xiao, Eric D. Peterson, Donghan M. Yang, Yang Xie
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This study introduces an innovative multi-agent ensemble method powered by Large Language Models (LLMs) to address the challenge of data labeling in large-scale Electronic Health Record (EHR) datasets. The manual labeling process is labor-intensive, time-consuming, expensive, and error-prone. To overcome this bottleneck, the authors developed an ensemble LLMs method that demonstrated effectiveness in two real-world tasks: labeling a large-scale unlabeled ECG dataset in MIMIC-IV and identifying social determinants of health (SDOH) from clinical notes. The study selected diverse open-source LLMs with satisfactory performance, treating each prediction as a vote and applying a majority voting mechanism. The ensemble LLMs application was implemented for EHR data labeling tasks, achieving an estimated accuracy of 98.2% in labeling the MIMIC-IV ECG dataset. The method also identified SDOH from social history sections with competitive performance. The study shows that the ensemble LLMs can outperform individual LLMs and reduce hallucination errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to help doctors and researchers label big amounts of medical data quickly and accurately. This is important because labeling data by hand takes a lot of time, money, and effort. The authors developed a special method that uses many different language models to work together and make decisions. They tested this method on two big projects: one to label heart rhythm records and another to identify things that affect people’s health. The results showed that their method can do this job just as well or even better than the best individual model. |
Keywords
» Artificial intelligence » Data labeling » Hallucination
