Summary of A Comparative Study on Automatic Coding Of Medical Letters with Explainability, by Jamie Glen et al.
A Comparative Study on Automatic Coding of Medical Letters with Explainability
by Jamie Glen, Lifeng Han, Paul Rayson, Goran Nenadic
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 study explores the application of Natural Language Processing (NLP) and machine learning (ML) techniques to automate medical letter coding, focusing on visualized explainability and lightweight local computer settings. The current manual process involves assigning codes to patient paperwork using standardized vocabularies like SNOMED CT. While preliminary research has shown promise in automatic coding using state-of-the-art ML models, real-world deployment is hindered by model complexity and size. To facilitate practical implementation, the study investigates solutions for local computer settings and explores the role of explainability for AI model transparency. The MIMIC-III database and HAN/HLAN network models are used for ICD code prediction, while mapping between ICD and SNOMED CT knowledge bases is also explored. Experiments demonstrate useful information provision for 97.98% of codes, shedding light on implementing automatic clinical coding in practice, such as hospital settings using local computers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to use computer programs (AI) to help doctors and nurses write medical reports more efficiently. Right now, writing these reports takes a lot of time because people have to assign special codes to every condition, procedure, and medicine mentioned in the report. Some research has been done on using AI for this task, but it’s not yet ready for real-world use because the computer programs are too complicated or big. The researchers want to make AI more practical by finding ways to simplify the process and make sure doctors understand how the AI is making decisions. They used a special database and some special computer models to test their ideas and found that the AI was able to provide useful information for most of the codes. |
Keywords
» Artificial intelligence » Machine learning » Natural language processing » Nlp
