Summary of Generation and De-identification Of Indian Clinical Discharge Summaries Using Llms, by Sanjeet Singh and Shreya Gupta and Niralee Gupta and Naimish Sharma and Lokesh Srivastava and Vibhu Agarwal and Ashutosh Modi
Generation and De-Identification of Indian Clinical Discharge Summaries using LLMs
by Sanjeet Singh, Shreya Gupta, Niralee Gupta, Naimish Sharma, Lokesh Srivastava, Vibhu Agarwal, Ashutosh Modi
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 explores the consequences of healthcare data breaches, particularly in India where rapid digitization is taking place. The average financial impact of a breach has been estimated to be around USD 10 million. To address this issue, researchers investigated the performance of de-identification algorithms on Indian health datasets. They found that existing algorithms trained on non-Indian datasets lack cross-institutional generalization and are vulnerable to data drift. The study also demonstrated potential risks associated with off-the-shelf de-identification systems. To overcome these limitations, the authors explored generating synthetic clinical reports using Large Language Models (LLMs) in an Indian context. Their experiments showed that generated reports can be used to create high-performing de-identification systems with good generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how healthcare data breaches can cause big problems for patients, doctors, and insurance companies. In India, where many hospitals are using computers more often, this is especially important. A lot of money can be lost because of a data breach – around USD 10 million on average. Some computer systems that hide personal information aren’t very good at keeping it safe when used in different places or settings. The researchers tested these systems and found they have some big problems. They also looked at how to make better de-identification algorithms by using computers to generate fake medical records. |
Keywords
» Artificial intelligence » Generalization
