dc.creator | Báez, Pablo | |
dc.creator | Villena, Fabián | |
dc.creator | Zúñiga, Karen | |
dc.creator | Jones, Natalia | |
dc.creator | Fernández, Gustavo | |
dc.creator | Durán, Manuel | |
dc.creator | Dunstan Escudero, Jocelyn Mariel | |
dc.date.accessioned | 2022-05-03T16:35:55Z | |
dc.date.accessioned | 2022-10-17T16:15:10Z | |
dc.date.available | 2022-05-03T16:35:55Z | |
dc.date.available | 2022-10-17T16:15:10Z | |
dc.date.created | 2022-05-03T16:35:55Z | |
dc.date.issued | 2021 | |
dc.identifier | Rev Med Chile 2021; 149: 1014-1022 | |
dc.identifier | 0034-9887 | |
dc.identifier | https://repositorio.uchile.cl/handle/2250/185227 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4420696 | |
dc.description.abstract | A 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. Aim: To build a linguistic resource of annotated medical
entities on texts produced in Chilean hospitals. Material and Methods: A
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. Results: An 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.
Conclusions: A 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. | |
dc.language | es | |
dc.publisher | Soc Medica Santiago | |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
dc.source | Revista Médica de Chile | |
dc.subject | Data curation | |
dc.subject | Data mining | |
dc.subject | Medical informatics | |
dc.subject | Natural language processing | |
dc.subject | Supervised machine learning | |
dc.title | Construcción de recursos de texto para la identificación automática de información clínica en narrativas no estructuradas | |
dc.type | Artículos de revistas | |