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.