Summary of Enhancing Multi-class Disease Classification: Neoplasms, Cardiovascular, Nervous System, and Digestive Disorders Using Advanced Llms, by Ahmed Akib Jawad Karim et al.
Enhancing Multi-Class Disease Classification: Neoplasms, Cardiovascular, Nervous System, and Digestive Disorders Using Advanced LLMs
by Ahmed Akib Jawad Karim, Muhammad Zawad Mahmud, Samiha Islam, Aznur Azam
First submitted to arxiv on: 19 Nov 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 A novel study explores the effectiveness of pre-trained language models in multi-class disease classification. Researchers compared four large language models (LLMs), including BioBERT, XLNet, BERT, and a custom model called Last-BERT, on a medical text corpus spanning five conditions. The results show that BioBERT, which was pre-trained on medical data, outperformed the other models with 97% accuracy. XLNet also demonstrated strong performance, despite not being trained on medical data. Another interesting finding is the competitive performance of Last-BERT, a custom model with fewer parameters, achieving 87.10% accuracy. The study highlights the importance of specialized models like BioBERT and suggests that more general solutions like XLNet can also be effective in medical domain tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists tested how well pre-trained language models could help diagnose different diseases. They looked at four special kinds of AI models called LLMs: BioBERT, XLNet, BERT, and Last-BERT. These models were trained on lots of text about medicine to see if they could accurately identify five specific diseases. The results show that one model, BioBERT, was super good at diagnosing diseases with 97% accuracy! Another model, XLNet, did really well too, even though it wasn’t trained on medical data. A third model, Last-BERT, also performed well and had fewer “building blocks” (parameters) than the other models. This study shows that special AI models like BioBERT are helpful for diagnosing diseases, but more general solutions like XLNet can work well too. |
Keywords
» Artificial intelligence » Bert » Classification
