MEDDOPROF explores the automatic detection of occupations and employment status, as well as their normalization or entity mapping, within medical documents in Spanish language in a corpus of clinical case reports from heterogeneous medical specialties. With the aim to include a comprehensive range of mentions of occupations and employment status, case reports from over 20 specialties are included in the corpus: infectious diseases (including Covid-19 case reports), cardiology, neurology, oncology, psychiatry, internal medicine, emergency and intensive care medicine, …
The task will be structured into three sub-tasks, each taking into account a particular practical user scenario of the resulting participant systems. These sub-tasks are independent and you don’t need to participate in all three of them.
Track 1 – MEDDOPROF-NER
Requires automatically finding mentions of occupations and classifying each of them as a profession (label PROFESION), an employment status (label SITUACION_LABORAL) or an activity (ACTIVIDAD). All occupation mentions are defined by their corresponding character offsets in UTF-8 plain text medical documents. Example (the start and the end character offsets are highlighted in bold):
Track 2 – MEDDOPROF-CLASS
Requires finding automatically mentions of occupations and determine whether they are related to the patient (label PACIENTE), to a family member (label FAMILIAR), to a health professional (label SANITARIO) or to someone else (label OTROS). Again, all occupation mentions are defined by their corresponding character offsets in UTF-8 plain text medical documents. Example (the start and the end character offsets are highlighted in bold):
Track 3 – MEDDOPROF-NORM
Requires mapping your predictions to one of the codes in a list of unique concept identifiers from the European Skills, Competences, Qualifications and Occupations (ESCO) classification and relevant SNOMED-CT terms.
