CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes

We created a dataset of clinical action items annotated over MIMIC-III. This
dataset, which we call CLIP, is annotated by physicians and covers 718
discharge summaries, representing 107,494 sentences. Annotations were
collected as character-level spans to discharge summaries after applying
surrogate generation to fill in the anonymized templates from MIMIC-III text
with faked data. We release these spans, their aggregation into sentence-level
labels, and the sentence tokenizer used to aggregate the spans and label
sentences. We also release the surrogate data generator, and the document IDs
used for training, validation, and test splits, to enable reproduction. The
spans are annotated with 0 or more labels of 7 different types, representing
the different actions that may need to be taken: Appointment, Lab, Procedure,
Medication, Imaging, Patient Instructions, and Other. We encourage the
community to use this dataset to develop methods for automatically extracting
clinical action items from discharge summaries.