Problem Statement

Clinical studies often require detailed patients’ information documented in clinical narratives. Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task to extract entities of interest (e.g., disease names, medication names and lab tests) from clinical narratives, thus to support clinical and translational research. Clinical notes have been analyzed in greater detail to harness important information for clinical research and other healthcare operations, as they depict rich, detailed medical information.

In this challenge, hackers are invited to extract all disease names from a given set of 20000 paragraphs/documents in the test set provided the labelled entities (diseases) for 30000 documents in the train set.

For example, here is a sentence from a clinical report:

We compared the inter-day reproducibility of post-occlusive reactive hyperemia (PORH) assessed by single-point laser Doppler flowmetry (LDF) and laser speckle contrast analysis (LSCI).

In the sentence given, reactive hyperemia (in bold) is the named entity with the type disease/indication.