Summary of Improving Icd Coding Using Chapter Based Named Entities and Attentional Models, by Abhijith R. Beeravolu et al.
Improving ICD coding using Chapter based Named Entities and Attentional Models
by Abhijith R. Beeravolu, Mirjam Jonkman, Sami Azam, Friso De Boer
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research introduces an enhanced approach to automatic ICD coding, a crucial task in natural language processing (NLP). Current methods rely on outdated and imbalanced datasets like MIMIC-III, resulting in micro-averaged F1 scores between 0.4 and 0.7 due to many false positives. The proposed method uses chapter-based named entities and attentional models to improve F1 scores. This approach categorizes discharge summaries into ICD-9 Chapters and develops attentional models with chapter-specific data, eliminating the need for external data. For categorization, Chapter-IV is used to de-bias and influence key entities and weights without neural networks, providing accurate thresholds and interpretability for human validation. The average Micro-F1 scores of 0.79 and 0.81 from the developed attentional models demonstrate significant performance improvements in ICD coding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a breakthrough in natural language processing (NLP) by improving automatic ICD coding, which is important for healthcare. Right now, most methods use old and imbalanced data that can’t accurately reflect real-life scenarios. This new approach uses special named entities and attention models to make better predictions. It works by categorizing medical records into groups and creating custom models for each group. This makes it easier to understand and validate the results. The new method is much more accurate than before, with an average score of 0.79 and 0.81. |
Keywords
» Artificial intelligence » Attention » Natural language processing » Nlp
