Summary of Synthetic4health: Generating Annotated Synthetic Clinical Letters, by Libo Ren et al.
Synthetic4Health: Generating Annotated Synthetic Clinical Letters
by Libo Ren, Samuel Belkadi, Lifeng Han, Warren Del-Pinto, Goran Nenadic
First submitted to arxiv on: 14 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 The paper introduces a novel approach to generate synthetic clinical letters that can be used in various applications such as model training, medical research, and teaching without compromising sensitive patient information. The authors explore different pre-trained language models (PLMs) for masking and generating text, focusing on Bio_ClinicalBERT due to its high performance. Various masking strategies are experimented with to create reliable and diverse synthetic clinical letters. The paper’s effectiveness is evaluated using both qualitative and quantitative methods, including a downstream task of Named Entity Recognition (NER). This work has the potential to improve medical research, patient care, and education. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates fake medical letters that can be used in many places without sharing people’s private information. To make these synthetic letters realistic, researchers tried different computer models that can write text. They chose a good model called Bio_ClinicalBERT and tested different ways to hide important words. The new letters were checked using two methods: one looks at how well the letters work, and another checks if they can be used for specific tasks like identifying medical terms. |
Keywords
» Artificial intelligence » Named entity recognition » Ner
