Summary of A Masked Language Model For Multi-source Ehr Trajectories Contextual Representation Learning, by Ali Amirahmadi (1) et al.
A Masked language model for multi-source EHR trajectories contextual representation learning
by Ali Amirahmadi, Mattias Ohlsson, Kobra Etminani, Olle Melander, Jonas Björk
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a novel approach to address challenges in utilizing electronic health records data for decision-making, specifically focusing on modeling interactions between diseases and interventions. By leveraging bidirectional transformers, which have effectively handled long-term dependencies, the authors mask one source of information (e.g., ICD10 codes) and train the model to predict it using other sources (e.g., ATC codes). This method has the potential to improve the accuracy and effectiveness of decision-making processes in healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special kinds of artificial intelligence called machine learning to make better decisions in healthcare. Right now, it’s hard to understand how different health problems and treatments work together. The authors are trying to solve this problem by creating a new kind of computer model that can learn from information like disease codes (ICD10) and treatment codes (ATC). They’re testing this approach using real medical data and hope it will help make better decisions in the future. |
Keywords
* Artificial intelligence * Machine learning * Mask
