Modeling Individual Cyclic Variation in Human Behavior

Proceedings of the ... International World-Wide Web Conference
Emma PiersonJure Leskovec

Abstract

Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual fea...Continue Reading

Citations

Sep 18, 2021·Journal of the American Medical Informatics Association : JAMIA·Kathy LiNoémie Elhadad
Oct 7, 2021·Journal of the American Medical Informatics Association : JAMIA·Adrienne PichonNoémie Elhadad

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Methods Mentioned

BETA
pregnancy tests

Software Mentioned

ARSER
CyHMMs
CyHMM

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