MIMIC-IV-Ext-22MCTS: A 22 Millions-Event Temporal Clinical Time-Series Dataset with Relative Timestamp

Clinical risk prediction based on machine learning algorithms plays a vital
role in modern healthcare. A crucial component in developing a reliable
prediction model is a high-quality dataset with time series clinical events.
In this work, we release such a dataset that consists of 22,588,586 clinical
time series events, which we term MIMIC-IV-Ext-22MCTS. Our source data are
discharge summaries selected from the well-known yet unstructured MIMIC-IV-
Note. We then extract clinical events as short text spans from the discharge
summaries, along with the timestamps of these events as temporal information
by contextual retrieval and Llama-3.1-8B.