Summary of Medpromptextract (medical Data Extraction Tool): Anonymization and Hi-fidelity Automated Data Extraction Using Nlp and Prompt Engineering, by Roomani Srivastava et al.
MedPromptExtract (Medical Data Extraction Tool): Anonymization and Hi-fidelity Automated data extraction using NLP and prompt engineering
by Roomani Srivastava, Suraj Prasad, Lipika Bhat, Sarvesh Deshpande, Barnali Das, Kshitij Jadhav
First submitted to arxiv on: 4 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR)
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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 presents MedPromptExtract, a completely automated method to efficiently extract data from discharge summaries while maintaining confidentiality. The method uses pre-existing tools like EIGEN and Natural Language Processing (NLP) to anonymize the summaries and extract custom clinical information from free-flowing text. Twelve features associated with the occurrence of Acute Kidney Injury were extracted, with high accuracy validated against clinicians’ annotations. The paper demonstrates the effectiveness of MedPromptExtract in extracting data from medical records, making it a valuable tool for digitizing medical records. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way to extract information from hospital records. This helps keep patient information safe while still being able to use this info to help doctors and researchers do their jobs better. The method uses special computer programs to look at the hospital records, find important details, and put them into a format that’s easy to understand. The results show that this method is very accurate and can be used to extract lots of different types of information. |
Keywords
* Artificial intelligence * Natural language processing * Nlp
