Summary of Automatic Extraction Of Disease Risk Factors From Medical Publications, by Maxim Rubchinsky et al.
Automatic Extraction of Disease Risk Factors from Medical Publications
by Maxim Rubchinsky, Ella Rabinovich, Adi Shraibman, Netanel Golan, Tali Sahar, Dorit Shweiki
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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 The proposed approach automates the identification of risk factors for diseases from medical literature by leveraging pre-trained models in the bio-medical domain. A multi-step system is introduced to identify relevant articles, classify them based on the presence of risk factor discussions, and extract specific risk factor information for a disease through a question-answering model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps doctors and researchers quickly find important information about diseases from medical articles. It uses special models trained for bio-medical tasks, making it easier to understand and use. The approach is designed to tackle the challenges of medical literature being diverse and unstructured, providing a valuable tool for the healthcare industry. |
Keywords
* Artificial intelligence * Question answering
