Summary of Improving Vte Identification Through Language Models From Radiology Reports: a Comparative Study Of Mamba, Phi-3 Mini, and Bert, by Jamie Deng et al.
Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT
by Jamie Deng, Yusen Wu, Yelena Yesha, Phuong Nguyen
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 machine learning approach is developed for accurate and timely identification of venous thromboembolism (VTE), a critical cardiovascular condition. The Mamba architecture-based classifier achieves remarkable results, with 97% accuracy and F1 score on the deep vein thrombosis (DVT) dataset and 98% accuracy and F1 score on the pulmonary embolism (PE) dataset. This approach eliminates the need for hand-engineered rules, reducing model complexity while maintaining comparable performance to previous hybrid methods. The study also evaluates a lightweight Large Language Model (LLM), Phi-3 Mini, in detecting VTE, which outperforms baseline BERT models but is computationally intensive due to its larger parameter set. Overall, the Mamba-based model offers an effective solution to VTE identification, overcoming limitations of previous approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to quickly and accurately diagnose a serious heart condition called venous thromboembolism (VTE). This helps doctors give better treatment. The new method uses a special kind of artificial intelligence called the Mamba architecture-based classifier. It works really well, getting almost all cases right on two different tests. This approach is simpler than previous methods and still gets good results. Another type of AI model, called a lightweight Large Language Model (LLM), Phi-3 Mini, was also tested. While it did well, it uses up a lot of computer power because it has many parameters. |
Keywords
» Artificial intelligence » Bert » F1 score » Large language model » Machine learning
