Summary of Question-answering Based Summarization Of Electronic Health Records Using Retrieval Augmented Generation, by Walid Saba et al.
Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation
by Walid Saba, Suzanne Wendelken, James. Shanahan
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel method for summarizing electronic health records (EHRs) using machine learning pipelines. The goal is to minimize ‘screen time’ for both patients and medical personnel by extracting important information from EHRs. Recent approaches have employed state-of-the-art neural models, but these have been limited by the difficulty of obtaining sufficient annotated data for training. Additionally, attention mechanisms in modern large language models (LLMs) can add quadratic complexity to the summarization process. To address these shortcomings, the authors combine semantic search, retrieval augmented generation (RAG), and question-answering using LLMs. The proposed approach involves extracting answers to specific questions deemed important by subject-matter experts (SMEs). This method is efficient, requiring minimal training, and avoids the ‘hallucination’ problem of LLMs while ensuring diversity in the summary. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps reduce the time spent reading electronic health records (EHRs) for patients and doctors. Currently, machines use advanced neural models to summarize EHRs, but this hasn’t been very effective because it’s hard to get enough training data. Also, these models can struggle with processing large amounts of information. To fix this, the researchers combined three techniques: searching for specific answers in EHRs, generating new text based on what was found, and asking questions about important topics. This new approach is efficient, doesn’t need much training, and avoids making things up while still providing different answers to the same question. |
Keywords
» Artificial intelligence » Attention » Hallucination » Machine learning » Question answering » Rag » Retrieval augmented generation » Summarization
