Summary of Zero- and Few-shot Named Entity Recognition and Text Expansion in Medication Prescriptions Using Chatgpt, by Natthanaphop Isaradech et al.
Zero- and Few-shot Named Entity Recognition and Text Expansion in Medication Prescriptions using ChatGPT
by Natthanaphop Isaradech, Andrea Riedel, Wachiranun Sirikul, Markus Kreuzthaler, Stefan Schulz
First submitted to arxiv on: 26 Sep 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 explores the application of Large Language Models (LLMs), specifically ChatGPT 3.5, in automatically structuring and expanding medication statements within discharge summaries. The goal is to make these statements easier to interpret for both humans and machines. To achieve this, the researchers employed Named-entity Recognition (NER) and Text Expansion (EX) techniques using a zero- and few-shot setting with varying prompt strategies. The study utilized 100 manually annotated and curated medication statements, evaluating NER performance through strict and partial matching. For EX, two experts assessed semantic equivalence between original and expanded statements. Model performance was measured by precision, recall, and F1 score. The results show that the best-performing NER prompt achieved an average F1 score of 0.94 in the test set, while the few-shot prompt demonstrated superior performance for EX, with an average F1 score of 0.87. Overall, this study demonstrates good performance for NER and EX tasks in free-text medication statements using ChatGPT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses computer programs to make it easier to understand medication information written in a confusing format. The goal is to help both people and computers better understand medical records. The researchers tested different ways of teaching the program, called ChatGPT 3.5, to recognize important words and phrases (like medicine names) and expand short statements into longer ones that are easier to read. They used a special set of sample data to see how well the program worked. The results show that this approach was successful in making the medication information more understandable. This could be important for improving patient safety, as it helps computers accurately process medical information. |
Keywords
» Artificial intelligence » F1 score » Few shot » Named entity recognition » Ner » Precision » Prompt » Recall
