Missing data imputation for noisy time-series data and applications in healthcare
This addresses missing data issues in healthcare monitoring, but it is incremental as it compares existing methods without introducing new ones.
The study compared imputation methods like MICE-RF and deep learning approaches for noisy, missing time-series healthcare data, finding that MICE-RF effectively imputed data and improved classification metrics, suggesting imputation can also provide denoising effects.
Healthcare time series data is vital for monitoring patient activity but often contains noise and missing values due to various reasons such as sensor errors or data interruptions. Imputation, i.e., filling in the missing values, is a common way to deal with this issue. In this study, we compare imputation methods, including Multiple Imputation with Random Forest (MICE-RF) and advanced deep learning approaches (SAITS, BRITS, Transformer) for noisy, missing time series data in terms of MAE, F1-score, AUC, and MCC, across missing data rates (10 % - 80 %). Our results show that MICE-RF can effectively impute missing data compared to deep learning methods and the improvement in classification of data imputed indicates that imputation can have denoising effects. Therefore, using an imputation algorithm on time series with missing data can, at the same time, offer denoising effects.