Summary of A Comparison Of Veterans with Problematic Opioid Use Identified Through Natural Language Processing Of Clinical Notes Versus Using Diagnostic Codes, by Terri Elizabeth Workman et al.
A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes
by Terri Elizabeth Workman, Joel Kupersmith, Phillip Ma, Christopher Spevak, Friedhelm Sandbrink, Yan Cheng Qing Zeng-Treitler
First submitted to arxiv on: 18 Jan 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 Machine learning researchers have long sought to leverage electronic health records (EHRs) for insights into opioid use disorder. A persistent challenge lies in the under-coding of this condition in EHR diagnoses, despite its prevalence in clinical notes. This study aims to address this limitation by developing a novel approach for identifying problematic opioid use from EHR data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Opioid use disorder is a serious issue that affects many people. To help understand and prevent it, scientists are looking at electronic health records (EHRs). The problem is that doctors often don’t write down the correct diagnosis, even though they may mention it in their notes. This makes it hard to study the condition. Researchers want to create new ways to find opioid use disorder in EHRs. |
Keywords
* Artificial intelligence * Machine learning
