MIMIC-III-Ext-tPatchGNN

This dataset is a curated subset of MIMIC-III (v1.4), specifically formatted
to facilitate reproducibility of the experiments in the work t-PatchGNN. It
serves as part of a benchmark designed for forecasting irregular multivariate
clinical time series, that is, given a set of historical Irregular
Multivariate Time Series (IMTS) observations and forecasting queries, the
forecasting problem aims to accurately forecast the values in correspondence
to these queries. This requires addressing key challenges such as missing
data, variable sampling rates, and complex temporal dependencies. The dataset
includes patient records with diverse physiological measurements, each sampled
at irregular intervals, reflecting real-world clinical scenarios. It is
structured to capture both short-term and long-term temporal patterns, making
it well-suited for evaluating machine learning models in medical time series
forecasting. By providing a standardized benchmark, this dataset aims to
advance research in predictive modeling for healthcare, enabling the
development of robust algorithms that can handle irregular and sparse clinical
data. The dataset's applications extend to critical areas such as early
disease detection, patient risk stratification, and treatment outcome
prediction, making it a valuable resource for the medical AI and machine
learning communities.