Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset

Irregularly sampled time series data occur in multiple scientific and industrial domains including finance, climate science and healthcare. In healthcare, electronic health records (EHR) have been widely adopted with the hope they would save time and improve the quality of patient care. The role of Artificial Intelligence (AI) in EHR is rapidly transforming the healthcare landscape, offering new opportunities to improve patient care, enhance decision-making, and optimize healthcare operations Shukla and Marlin (2022). Time-series data is routinely collected in various healthcare settings where different measurements are recorded for patients throughout their course of stay. Predicting clinical outcomes like mortality, decompensation, length of stay, and disease risk from such complex multivariate time-series data can facilitate both effective management of critical care units and automatic personalized treatment recommendations for patients Tipirneni et al. (2022). However, modeling time series data subject to irregular sampling poses a significant challenge to machine learning models that assume fully observed, fixed-size feature representations Shukla and Marlin (2021).