Summary of Natural Language Processing For Analyzing Electronic Health Records and Clinical Notes in Cancer Research: a Review, by Muhammad Bilal et al.
Natural Language Processing for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review
by Muhammad Bilal, Ameer Hamza, Nadia Malik
First submitted to arxiv on: 29 Oct 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 review examines the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. Analyzing 94 studies published between 2019 and 2024, the authors identify a growing trend in NLP applications for breast, lung, and colorectal cancers, with information extraction and text classification being the most common tasks. The review highlights a shift from rule-based to advanced machine learning techniques, particularly transformer-based models. Key challenges include limited generalizability of proposed solutions and the need for improved integration into clinical workflows. The authors conclude that NLP techniques show significant potential in enhancing cancer diagnosis, treatment, and patient outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This review looks at how computers can help doctors with cancer research using medical records and notes. Researchers studied 94 papers from 2019 to 2024 and found that computers are getting better at finding important information in these records. They’re particularly good at helping with breast, lung, and colon cancers. The computer programs are becoming more advanced and can even understand complex language like doctors use. However, there’s still work to be done to make sure these programs can be used in real-life hospitals. |
Keywords
» Artificial intelligence » Machine learning » Natural language processing » Nlp » Text classification » Transformer
