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This article introduces the sequential Kalman filter, a computationallyscalable approach for online changepoint detection with temporally correlateddata. The temporal correlation was not considered in the Bayesian onlinechangepoint detection approach due to the large computational cost. Motivatedby detecting COVID-19 infections for dialysis patients from massivelongitudinal health records with a large number of covariates, we develop ascalable approach to detect multiple changepoints from correlated data bysequentially stitching Kalman filters of subsequences to compute the jointdistribution of the observations, which has linear computational complexitywith respect to the number of observations between the last detectedchangepoint and the current observation at each time point, withoutapproximating the likelihood function. Compared to other online changepointdetection methods, simulated experiments show that our approach is more precisein detecting single or multiple changes in mean, variance, or correlation fortemporally correlated data. Furthermore, we propose a new way to integrateclassification and changepoint detection approaches that improve the detectiondelay and accuracy for detecting COVID-19 infection compared to otheralternatives.

Sequential Kalman filter for fast online changepoint detection in longitudinal health records