Summary of Corelation: Boosting Automatic Icd Coding Through Contextualized Code Relation Learning, by Junyu Luo et al.
CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning
by Junyu Luo, Xiaochen Wang, Jiaqi Wang, Aofei Chang, Yaqing Wang, Fenglong Ma
First submitted to arxiv on: 24 Feb 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 This paper proposes a novel approach to automatic International Classification of Diseases (ICD) coding, which is crucial for extracting relevant information from clinical notes. The existing methods insufficiently model the intricate relationships among ICD codes and overlook the importance of context in clinical notes. The proposed framework employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations. The evaluation on six public ICD coding datasets demonstrates the effectiveness of the approach compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding medical records. Right now, computers have trouble understanding these records because they don’t know how different pieces of information relate to each other. This makes it hard for them to accurately record and bill for medical care. The researchers came up with a new way to help computers understand this information by considering the context in which it is presented. They tested their approach on several datasets and found that it worked better than existing methods. |
Keywords
* Artificial intelligence * Classification
