Summary of Medcoder: a Generative Ai Assistant For Medical Coding, by Krishanu Das Baksi et al.
MedCodER: A Generative AI Assistant for Medical Coding
by Krishanu Das Baksi, Elijah Soba, John J. Higgins, Ravi Saini, Jaden Wood, Jane Cook, Jack Scott, Nirmala Pudota, Tim Weninger, Edward Bowen, Sanmitra Bhattacharya
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 presents a new approach to automating medical coding using Generative Artificial Intelligence (AI). Medical coding is crucial for standardizing clinical data and communication but often time-consuming and prone to errors. Traditional Natural Language Processing (NLP) methods struggle with automating coding due to the large label space, lengthy text inputs, and lack of supporting evidence annotations that justify code selection. The proposed framework, MedCodER, combines extraction, retrieval, and re-ranking techniques to achieve a micro-F1 score of 0.60 on International Classification of Diseases (ICD) code prediction, outperforming state-of-the-art methods. A new dataset containing medical records annotated with disease diagnoses, ICD codes, and supporting evidence texts is also introduced. The framework’s performance depends on the integration of its core components, as evaluated in isolation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about making it easier to code medical information using artificial intelligence. Medical coding is important for sharing patient data between doctors but can be time-consuming and prone to mistakes. Traditional computer methods struggle with doing this job because there are many possible codes, the text is long, and there isn’t enough information to help make good choices. The new system, MedCodER, uses three techniques together to do a better job of predicting medical codes. It also introduces a new dataset of medical records that have been labeled with diagnoses, codes, and supporting evidence. This shows that the system works best when all its parts are working together. |
Keywords
* Artificial intelligence * Classification * F1 score * Natural language processing * Nlp
