• Pablo Báez, Fabián Villena, Karen Zúñiga, Natalia Jones, Gustavo Fernández, Manuel Durán, and Jocelyn Dunstan.
• Centro de Informática Médica y Telemedicina, Facultad de Medicina, Universidad de Chile, Santiago, Chile.
• Rev Med Chil. 2021 Jul 1; 149 (7): 1014-1022.

BackgroundA significant proportion of the clinical record is in free text format, making it difficult to extract key information and make secondary use of patient data. Automatic detection of information within narratives initially requires humans, following specific protocols and rules, to identify medical entities of interest.AimTo build a linguistic resource of annotated medical entities on texts produced in Chilean hospitals.Material And MethodsA clinical corpus was constructed using 150 referrals in public hospitals. Three annotators identified six medical entities: clinical findings, diagnoses, body parts, medications, abbreviations, and family members. An annotation scheme was designed, and an iterative approach to train the annotators was applied. The F1-Score metric was used to assess the progress of the annotator's agreement during their training.ResultsAn average F1-Score of 0.73 was observed at the beginning of the project. After the training period, it increased to 0.87. Annotation of clinical findings and body parts showed significant discrepancy, while abbreviations, medications, and family members showed high agreement.ConclusionsA linguistic resource with annotated medical entities on texts produced in Chilean hospitals was built and made available, working with annotators related to medicine. The iterative annotation approach allowed us to improve performance metrics. The corpus and annotation protocols will be released to the research community.

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