SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks

• URL: http://arxiv.org/abs/2001.10071v1
• Date: Mon, 27 Jan 2020 20:39:32 GMT
• Title: SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks
• Authors: Lucas Emanuel Silva e Oliveira, Ana Carolina Peters, Adalniza Moura Pucca da Silva, Caroline P. Gebeluca, Yohan Bonescki Gumiel, Lilian Mie Mukai Cintho, Deborah Ribeiro Carvalho, Sadid A. Hasan, Claudia Maria Cabral Moro
• Abstract summary: SemClinBr is a corpus that has 1,000 clinical notes, labeled with 65,117 entities and 11,263 relations. This work is the SemClinBr, a corpus that has 1,000 clinical notes, labeled with 65,117 entities and 11,263 relations.
• Score: 0.7311642662742726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multi-purpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. In this study, we developed a semantically annotated corpus using clinical texts from multiple medical specialties, document types, and institutions. We present the following: (1) a survey listing common aspects and lessons learned from previous research, (2) a fine-grained annotation schema which could be replicated and guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. The result of this work is the SemClinBr, a corpus that has 1,000 clinical notes, labeled with 65,117 entities and 11,263 relations, and can support a variety of clinical NLP tasks and boost the EHR's secondary use for the Portuguese language.

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