Summary of Meddec: a Dataset For Extracting Medical Decisions From Discharge Summaries, by Mohamed Elgaar et al.
MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries
by Mohamed Elgaar, Jiali Cheng, Nidhi Vakil, Hadi Amiri, Leo Anthony Celi
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 proposes a new dataset called “MedDec” for medical decision extraction, which involves extracting and classifying different types of medical decisions from clinical notes. The MedDec dataset contains annotated clinical notes of eleven phenotypes (diseases) with ten types of medical decisions. The authors introduce the task of joint medical decision extraction and classification, providing a comprehensive analysis of the dataset, a baseline span detection model, and evaluating recent span detection approaches. The findings shed light on the complexities in clinical decision extraction and enable future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical decisions are crucial for people’s health, and understanding how doctors make these decisions is vital. This paper creates a new dataset called “MedDec” that helps researchers learn more about medical decision-making. MedDec has notes from different diseases with labeled doctor decisions. The goal is to find and categorize these decisions in the notes. The paper shares findings on how to analyze this data, uses existing models as examples, and shows how complex some of the data can be. |
Keywords
» Artificial intelligence » Classification
