Summary of Understanding Missingness in Time-series Electronic Health Records For Individualized Representation, by Ghadeer O. Ghosheh et al.
Understanding Missingness in Time-series Electronic Health Records for Individualized Representation
by Ghadeer O. Ghosheh, Jin Li, Tingting Zhu
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 models are revolutionizing healthcare, but there’s a crucial step missing: representing missingness patterns in Electronic Health Records (EHR) data. Current research neglects individualized missingness representation, hindering the full potential of machine learning applications. This paper highlights new insights into missingness patterns and implications for real-world EHRs, bridging the gap between theory and practice. By shedding light on missingness, we can unlock directions for better predictive modeling and true personalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about how doctors and computers work together to make personalized medicine a reality. Right now, there are many ways that computers can help with medical decisions, but one important step is missing: helping computers understand why some information might be missing from patient records. This matters because missing data can mean computers don’t get the whole picture of what’s going on with each person. The researchers in this paper looked at how often and why certain types of data are missing from real patient records, and they hope their findings will help make better computer systems that really understand individual patients. |
Keywords
* Artificial intelligence * Machine learning
