  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.
