Time-Dependent Iterative Imputation for Multivariate Longitudinal Clinical Data

Omer Noy, Ron Shamir

Keywords: Missing Data Imputation, Machine Learning for Healthcare

TL;DR: A new simple method for imputing missing values in time series clinical data obtains improved results on real-world datasets.

Abstract: Missing data is a major challenge in clinical research. In electronic medical records, often a large fraction of the values in laboratory tests and vital signs are missing. The missingness can lead to biased estimates and limit our ability to draw conclusions from the data. Additionally, many machine learning algorithms can only be applied to complete datasets. A common solution is data imputation, the process of filling-in the missing values. However, some of the popular imputation approaches perform poorly on clinical data. We developed a simple new approach, Time-Dependent Iterative imputation (TDI), which offers a practical solution for imputing time-series data. It addresses both multivariate and longitudinal data, by integrating forward-filling and Iterative Imputer. The integration employs a patient, variable, and observation-specific dynamic weighting strategy, based on the clinical patterns of the data, including missing rates and measurement frequency. We tested TDI on randomly masked clinical datasets. When applied to a cohort consisting of more than 500,000 patient observations from MIMIC III, our approach outperformed state-of-the-art imputation methods for 25 out of 30 clinical variables, with an overall root-mean-squared-error of 0.63, compared to 0.85 for SoftImpute, the second best method. MIMIC III and COVID-19 inpatient datasets were used to perform prediction tasks. Importantly, these tests demonstrated that TDI imputation can lead to improved risk prediction.

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