Summary of Outlier Ranking in Large-scale Public Health Streams, by Ananya Joshi et al.
Outlier Ranking in Large-Scale Public Health Streams
by Ananya Joshi, Tina Townes, Nolan Gormley, Luke Neureiter, Roni Rosenfeld, Bryan Wilder
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel task for algorithms to rank univariate outlier detection method outputs applied to multiple public health data streams, helping disease control experts identify important outliers amidst thousands of tied results. The proposed algorithm leverages hierarchical networks and extreme value analysis, outperforming traditional methods across various metrics in a human-expert evaluation using real-world public health data. The open-source Python implementation has been widely adopted since April 2023, enabling organizations to create rankings from their tailored univariate method outputs and accelerate outlier investigation by a factor of 9.1x. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created an algorithm that helps experts find important outliers in big datasets. They had to deal with thousands of results that were equally unusual, so they developed a way for algorithms to rank these results based on their importance. The new approach worked well and was tested using real-world data from public health streams. |
Keywords
» Artificial intelligence » Outlier detection
