Summary of A Novel Compact Llm Framework For Local, High-privacy Ehr Data Applications, by Yixiang Qu et al.
A Novel Compact LLM Framework for Local, High-Privacy EHR Data Applications
by Yixiang Qu, Yifan Dai, Shilin Yu, Pradham Tanikella, Travis Schrank, Trevor Hackman, Didong Li, Di Wu
First submitted to arxiv on: 3 Dec 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 A novel compact Large Language Model (LLM) framework is presented for local deployment in healthcare settings with strict privacy requirements and limited computational resources. The proposed preprocessing technique uses information extraction methods to filter and emphasize critical information in clinical notes, enhancing the performance of smaller LLMs on Electronic Health Records (EHR) data. This study evaluates the framework using zero-shot and few-shot learning paradigms on private and publicly available datasets, including MIMIC-IV. The results demonstrate that the preprocessing approach significantly boosts the prediction accuracy of smaller LLMs, making them suitable for high-privacy, resource-constrained applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about a special kind of computer model called a Large Language Model (LLM). These models are very good at understanding human language, but they need powerful computers to work. The problem is that some places don’t have those kinds of computers or want to keep their data private. This paper solves this problem by creating a smaller LLM that can work on regular computers and doesn’t use much power. They also found a way to make the model understand clinical notes better, which helps it predict things more accurately. The study shows that these smaller models can be very good at predicting things even when they don’t have access to a lot of data. |
Keywords
» Artificial intelligence » Few shot » Large language model » Zero shot
