MEDDOPROF corpus: training set + complementary entities

The MEDDOPROF Shared Task tackles the detection of occupations and employment statuses in clinical cases in Spanish from different specialties. Systems capable of automatically processing clinical texts are of interest to the medical community, social workers, researchers, the pharmaceutical industry, computer engineers, AI developers, policy makers, citizen’s associations and patients. Additionally, other NLP tasks (such as anonymization) can also benefit from this type of data.

MEDDOPROF has three different sub-tasks:

1) MEDDOPROF-NER: Participants must find the beginning and end of occupation mentions and classify them as PROFESION (PROFESSION), SITUACION_LABORAL (WORKING_STATUS) or ACTIVIDAD (ACTIVIDAD).

2) MEDDOPROF-CLASS: Participants must find the beginning and end of occupation mentions and classify them according to their referent (PACIENTE [patient], FAMILIAR [family member], SANITARIO [health professional] or OTRO [other]).

3) MEDDOPROF-NORM: Participants must find the beginning and end of occupation mentions and normalize them according to a reference codes list.

UPDATE 20/05/21: We have updated the .zip file to include additional mentions of automatically labelled annotations to complement the Gold Standard MEDDOPROF corpus. This complement is called MEDDOPROF-CE (Complementary Entities).

The CE version of the training data includes the Shared Task's original manual annotations (with the labels for task one and two joint together, e.g. "PACIENTE-PROFESION") and automatically generated clinical and linguistic entities. All in all, nine new entity types have been included: "síntoma" (symptom), "enfermedad" (disease), "procedimiento" (procedure), "fármaco" (drug), "org_vivo" (living organisms), "neg"/"nsco" (negation trigger and scope) and "unc"/"usco" (uncertainty trigger and scope).

The entities in the MEDDOPROF-CE version will not be evaluated in the task, but they can be used to test the impact of other entity types in the Shared Task's tracks or for information discovery. We encourage participants to be creative and incorporate these additional layers into their systems as they wish.

Please cite if you use this resource:

Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and Martin Krallinger. NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. In Procesamiento del Lenguaje Natural, 67. 2021.

Resources:

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