The adoption of electronic health records has increased the volume of clinical data, which has opened an opportunity for healthcare research. There are several biomedical annotation systems that have been used to facilitate the analysis of clinical data. However, there is a lack of clinical annotation comparisons to select the most suitable tool for a specific clinical task. In this work, we used clinical notes from the MIMIC-III database and evaluated three annotation systems to identify four types of entities: (1) procedure, (2) disorder, (3) drug, and (4) anatomy. Our preliminary results demonstrate that BioPortal performs well when extracting disorder and drug. This can provide clinical researchers with real-clinical insights into patient's health patterns and it may allow to create a first version of an annotated dataset.
|Título de la publicación alojada||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Editores||Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu|
|Editorial||Institute of Electrical and Electronics Engineers Inc.|
|Número de páginas||3|
|ISBN (versión digital)||9781728118673|
|Estado||Publicada - nov. 2019|
|Publicado de forma externa||Sí|
|Evento||2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, Estados Unidos|
Duración: 18 nov. 2019 → 21 nov. 2019
Serie de la publicación
|Nombre||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Conferencia||2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Período||18/11/19 → 21/11/19|
Nota bibliográficaFunding Information:
Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA183962. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
© 2019 IEEE.