Outlier Ranking for Large-Scale Public Health Data
Keywords: outlier ranking, data streams, big data, public health, public health systems
TL;DR: We developed and deployed a novel ranking and evaluation method to enable disease control experts to identify outliers worth investigating, like those corresponding to data quality issues or disease outbreaks, from millions of data points.
Abstract: [Already accepted at AAAI '24 as noted by Dr. Dacon]
Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks. However, they can only examine a few of the thousands of maximally-tied outliers returned by univariate outlier detection methods applied to large-scale public health data streams. To help experts distinguish the most important outliers from these thousands of tied outliers, we propose a new task for algorithms to \textbf{rank} the outputs of any univariate method applied to each of many streams. Our novel algorithm for this task, which leverages hierarchical networks and extreme value analysis, performed the best across traditional outlier detection metrics in a human-expert evaluation using public health data streams. Most importantly, experts have used our open-source Python implementation since April 2023 and report identifying outliers worth investigating 9.1x faster than their prior baseline. Other organizations can readily adapt this implementation to create rankings from the outputs of their tailored univariate methods across large-scale streams.
Submission Number: 5
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