Summary of Transfer Learning For the Prediction Of Entity Modifiers in Clinical Text: Application to Opioid Use Disorder Case Detection, by Abdullateef I. Almudaifer et al.
Transfer Learning for the Prediction of Entity Modifiers in Clinical Text: Application to Opioid Use Disorder Case Detection
by Abdullateef I. Almudaifer, Whitney Covington, JaMor Hairston, Zachary Deitch, Ankit Anand, Caleb M. Carroll, Estera Crisan, William Bradford, Lauren Walter, Eaton Ellen, Sue S. Feldman, John D. Osborne
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper presents a novel approach to identifying modifiers of clinical entities in medical text, such as negations, uncertainties, and severities. Current methods rely on manual feature engineering or regularization techniques, which can be limited and require domain-specific knowledge. The proposed model leverages contextual information from the surrounding text and entity relationships to accurately identify modifiers, improving overall clinical entity semantics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us better understand medical texts by recognizing words that change the meaning of clinical entities. Right now, computers struggle to find these modifier words, like “not” or “might”, which can completely flip our understanding of what a doctor is saying. The new approach uses computer vision techniques and looks at how words relate to each other to get it right. |
Keywords
* Artificial intelligence * Feature engineering * Regularization * Semantics
