  A crucial step within secondary analysis of electronic health records (EHRs)
is to identify the patient cohort under investigation. While EHRs contain
medical billing codes that aim to represent the conditions and treatments
patients may have, much of the information is only present in the patient
notes. Therefore, it is critical to develop robust algorithms to infer
patients' conditions and treatments from their written notes. In this paper, we
introduce a dataset for patient phenotyping, a task that is defined as the
identification of whether a patient has a given medical condition (also
referred to as clinical indication or phenotype) based on their patient note.
Nursing Progress Notes and Discharge Summaries from the Intensive Care Unit of
a large tertiary care hospital were manually annotated for the presence of
several high-context phenotypes relevant to treatment and risk of
re-hospitalization. This dataset contains 1102 Discharge Summaries and 1000
Nursing Progress Notes. Each Discharge Summary and Progress Note has been
annotated by at least two expert human annotators (one clinical researcher and
one resident physician). Annotated phenotypes include treatment non-adherence,
chronic pain, advanced/metastatic cancer, as well as 10 other phenotypes. This
dataset can be utilized for academic and industrial research in medicine and
computer science, particularly within the field of medical natural language
processing.
