Summary of Neural Machine Translation Of Clinical Procedure Codes For Medical Diagnosis and Uncertainty Quantification, by Pei-hung Chung et al.
Neural machine translation of clinical procedure codes for medical diagnosis and uncertainty quantification
by Pei-Hung Chung, Shuhan He, Norawit Kijpaisalratana, Abdel-badih el Ariss, Byung-Jun Yoon
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed Clinical Decision Support System (CDSS) integrates system-generated recommendations with medical expertise to enhance clinician decision-making. The CDSS leverages AI models to reduce diagnostic uncertainty, particularly in complex emergency scenarios where information is limited. By quantifying uncertainties in patient outcomes predicted by neural machine translation based on the ICD-9 code of procedures, the framework promises to be practical and beneficial in real-world medical care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new Clinical Decision Support System helps doctors make better decisions by combining computer suggestions with their own expertise. This system uses artificial intelligence to reduce uncertainty in diagnosis, especially in emergency situations where there is limited information. The goal is to create a useful tool for real-world medicine that can be used quickly and easily. |
Keywords
* Artificial intelligence * Translation
