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Post-discovery Analysis of Anomalous Subsets
Isaiah Onando Mulang' · William Ogallo · Girmaw Abebe Tadesse · Aisha Walcott-Bryant

Analyzing the behavior of a population in response to disease and interventions is critical to unearth variability in healthcare as well as understand sub-populations that require specialized attention, but also to assist in designing future interventions. Two aspects become very essential in such analysis namely: i) Discovery of differentiating patterns exhibited by sub-populations, and ii) Characterization of the identified subpopulations. For the discovery phase, an array of approaches in the anomalous pattern detection literature have been employed to reveal differentiating patterns, especially to identify anomalous subgroups. However, these techniques are limited to describing the anomalous subgroups and offer little in form of insightful characterization, thereby limiting interpretability and understanding of these data-driven techniques in clinical practices. In this work, we propose an analysis of differentiated output (rather than discovery) and quantify anomalousness similarly to the counter-factual setting. To this end we design an approach to perform post-discovery analysis of anomalous subsets, in which we initially identify the most important features on the anomalousness of the subsets, then by perturbation, the approach seeks to identify the least number of changes necessary to lose anomalousness. Our approach is presented and the evaluation results on the 2019 MarketScan Commercial Claims and Medicare data, show that extra insights can be obtained by extrapolated examination of the identified subgroups.

Author Information

Isaiah Onando Mulang' (IBM Research Africa)
William Ogallo (IBM Research)
Girmaw Abebe Tadesse (IBM Research | Africa)
Aisha Walcott-Bryant (IBM Research)

I am a research scientist and manager at IBM Research Africa - Nairobi, Kenya. I lead a team of phenomenal, brilliant researchers and engineers that use AI, Blockchain, and other technologies to develop innovations in Global Health, Water Access and Management, and Climate. I earned my PhD in the Electrical Engineering and Computer Science Department at MIT in robotics, as a member of the Computer Science and Artificial Intelligent lab (CSAIL).

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