Challenges in Imputation of ICU Time-Series Data: A Comparison of Classical and Machine Learning Approaches

Handling missing data is a major challenge in machine learning, particularly in clinical settings using electronic health records like ICU time-series data. Advanced imputation techniques, such as Bidirectional Recurrent Imputation for Time Series (BRITS), Self-Attention-based Imputation for Time Series (SAITS), and Multi-directional Recurrent Neural Network (M-RNN), show strong performance but are influenced by dataset characteristics like missing patterns and feature distributions. This study compares ten imputation methods on the public MIMIC-III database and COPRA, a local hospital dataset. We evaluate classical approaches (Zero, Mean, Median, Last), advanced models (BRITS, SAITS, M-RNN), and their transfer learning (TL) adaptations (TranBRITS, TranSAITS, TranM-RNN). TL was implemented by transforming COPRA to align with MIMIC-III, enabling fine-tuning of pre-trained models. TranSAITS achieved the best performance on the transformed dataset (M->C). Notably, the simple Last method also performed competitively for vital signs, underscoring its potential for real-time clinical use where low complexity and efficiency are key.