Phenotype Annotations for Patient Notes in the MIMIC-III Database

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.

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. Patient notes of MIMIC-III, a dataset
collected from Intensive Care Units of a large tertiary care hospital in
Boston, were manually annotated for the presence of several high-context
phenotypes relevant to treatment and risk of re-hospitalization.

Each note has been annotated by 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.