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 whether
a patient has a given phenotype (also referred to as indication) 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