title: Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data.
abstract: Most clinical and biomedical data contain missing values. A patient's record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined. Further, the data are not simply missing at random. Instead the measurement of a variable such as blood glucose may depend on its prior values as well as that of other variables. These dependencies exist across time as well, but current methods have yet to incorporate these temporal relationships as well as multiple types of missingness. To address this, we propose an imputation method (FLk-NN) that incorporates time lagged correlations both within and across variables by combining two imputation methods, based on an extension to k-NN and the Fourier transform. This enables imputation of missing values even when all data at a time point is missing and when there are different types of missingness both within and across variables. In comparison to other approaches on three biological datasets (simulated and actual Type 1 diabetes datasets, and multi-modality neurological ICU monitoring) the proposed method has the highest imputation accuracy. This was true for up to half the data being missing and when consecutive missing values are a significant fraction of the overall time series length.
authors.affiliation: Department of Computer Science, Stevens Institute of Technology, NJ, United States. Electronic address: srahman1@stevens.edu.
authors.forename: Shah Atiqur
authors.initials: SA
authors.lastname: Rahman
authors.affiliation: Department of Computer Science, Stevens Institute of Technology, NJ, United States. Electronic address: yhuang23@stevens.edu.
authors.forename: Yuxiao
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Division of Critical Care Neurology, Department of Neurology, Columbia University, College of Physicians and Surgeons, New York, NY, United States. Electronic address: jc1439@cumc.columbia.edu.
authors.forename: Jan
authors.initials: J
authors.lastname: Claassen
authors.affiliation: Dexcom Inc., San Diego, CA, United States. Electronic address: nheintzman@Dexcom.com.
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Heintzman
authors.affiliation: Department of Computer Science, Stevens Institute of Technology, NJ, United States. Electronic address: skleinbe@stevens.edu.
authors.forename: Samantha
authors.initials: S
authors.lastname: Kleinberg
mesh_terms: D005583:Fourier Analysis; D008962:Models, Theoretical
keywords: Biomedical data; Imputation; Missing data; Time series