Summary of Clustering Of Timed Sequences — Application to the Analysis Of Care Pathways, by Thomas Guyet et al.
Clustering of timed sequences – Application to the analysis of care pathways
by Thomas Guyet, Pierre Pinson, Enoal Gesny
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 educators can learn from this research, which aims to uncover typical care pathways in hospital settings by analyzing patient data. The authors adapt two time-series methods – the drop-DTW metric and the DBA approach – to cluster timed sequences of events, creating original clustering algorithms for this purpose. These approaches are tested on both synthetic and real-world datasets, demonstrating their effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps improve healthcare by understanding how doctors and nurses work together in hospitals. By looking at patient data, we can figure out common ways that patients receive care. This is important because it lets us know what works well and what doesn’t, so we can make better decisions about patient care. The authors use special computer algorithms to group similar events together, which helps us understand how care pathways are usually structured. |
Keywords
» Artificial intelligence » Clustering » Machine learning » Time series
