Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Lucas Emanuel Silva e Oliveira"'
Autor:
Lucas Emanuel Silva e Oliveira, Ana Carolina Peters, Adalniza Moura Pucca da Silva, Caroline Pilatti Gebeluca, Yohan Bonescki Gumiel, Lilian Mie Mukai Cintho, Deborah Ribeiro Carvalho, Sadid Al Hasan, Claudia Maria Cabral Moro
Publikováno v:
Journal of Biomedical Semantics, Vol 13, Iss 1, Pp 1-19 (2022)
Abstract Background The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and ev
Externí odkaz:
https://doaj.org/article/dca2a5a5934743c598bfb5786c86df86
Autor:
Claudia Maria Cabral Moro, Lucas Emanuel Silva e Oliveira, Yohan Bonescki Gumiel, Lucas Ferro Antunes de Oliveira, Deborah Ribeiro Carvalho
Publikováno v:
Research on Biomedical Engineering. 36:267-276
Natural language processing techniques are essential for unlocking patients’ data from electronic health records. An important NLP task is the ability to recognize morphosyntactic information from the texts, a process called part-of-speech (POS) ta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f13712ad973e0cfa9aa2d1d5ce10019
https://doi.org/10.1007/s42600-020-00067-7
https://doi.org/10.1007/s42600-020-00067-7
Autor:
Lucas Emanuel Silva e Oliveira, Mayara Aparecida Passaura da Luz, Yohan Bonescki Gumiel, Elisa Terumi Rubel Schneider, Claudia Maria Cabral Moro, Emerson Cabrera Paraiso
Publikováno v:
Intelligent Systems ISBN: 9783030916985
Question answering (QA) systems aim to answer human questions made in natural language. This type of functionality can be very useful in the most diverse application domains, such as the biomedical and clinical. Considering the clinical context, wher
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d753b1c4586671ceff8883ec8fa325e
https://doi.org/10.1007/978-3-030-91699-2_10
https://doi.org/10.1007/978-3-030-91699-2_10
Autor:
Vincent Claveau, Yohan Bonescki Gumiel, Natalia Grabar, Deborah Ribeiro Carvalho, Clément Dalloux, Claudia Maria Cabral Moro, Lucas Emanuel Silva e Oliveira
Publikováno v:
Natural Language Engineering
Natural Language Engineering, 2020, ⟨10.1017/S1351324920000352⟩
Natural Language Engineering, Cambridge University Press (CUP), 2020, ⟨10.1017/S1351324920000352⟩
Natural Language Engineering, 2020, ⟨10.1017/S1351324920000352⟩
Natural Language Engineering, Cambridge University Press (CUP), 2020, ⟨10.1017/S1351324920000352⟩
Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contribu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04c79697a19f1a36bc122a0351942b5c
https://hal.science/hal-03021033
https://hal.science/hal-03021033
Autor:
Julien Knafou, Elisa Terumi Rubel Schneider, Emerson Cabrera Paraiso, Lucas Ferro Antunes de Oliveira, Jenny Copara, Douglas Teodoro, João Vitor Andrioli de Souza, Yohan Bonescki Gumiel, Lucas Emanuel Silva e Oliveira, Claudia Maria Cabral Moro Barra
Publikováno v:
ClinicalNLP@EMNLP
Proceedings of the 3rd Clinical Natural Language Processing Workshop pp. 65-72
Proceedings of the 3rd Clinical Natural Language Processing Workshop pp. 65-72
With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity reco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55b44f859509e35b921e79b70eb3eaa5
https://doi.org/10.18653/v1/2020.clinicalnlp-1.7
https://doi.org/10.18653/v1/2020.clinicalnlp-1.7
Autor:
Lucas Emanuel Silva, E Oliveira, Yohan Bonescki, Gumiel, Arnon Bruno Ventrilho, Dos Santos, Lilian Mie Mukai, Cintho, Deborah Ribeiro, Carvalho, Sadid A, Hasan, Claudia Maria Cabral, Moro
Publikováno v:
Studies in health technology and informatics. 264
In this paper, we trained a set of Portuguese clinical word embedding models of different granularities from multi-specialty and multi-institutional clinical narrative datasets. Then, we assessed their impact on a downstream biomedical NLP task of Ur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::0a92c57baa7c637c717be44a86de5404
https://pubmed.ncbi.nlm.nih.gov/31437898
https://pubmed.ncbi.nlm.nih.gov/31437898
Autor:
Lucas Brehm, Ronnau, Fernanda Broering Gomes, Torres, Lucas Emanuel Silva, E Oliveira, Denilsen Carvalho, Gomes, Marcia Regina, Cubas, Claudia, Moro
Publikováno v:
Studies in health technology and informatics. 264
This study describes MappICNP, an automatic method for mapping between Brazilian Portuguese clinical narratives in free text and International Classification for Nursing Practice (ICNP) concepts. It's composed of six natural language processing rules
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::eaf8a322a7abb417914b60d20683af31
https://pubmed.ncbi.nlm.nih.gov/31438227
https://pubmed.ncbi.nlm.nih.gov/31438227
Autor:
Lucas Emanuel Silva e Oliveira, João Vitor Andrioli de Souza, Claudia Maria Cabral Moro, Yohan Bonescki Gumiel
Publikováno v:
Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2019).
Considering the difficulties of extracting entities from Electronic Health Records (EHR) texts in Portuguese, we explore the Conditional Random Fields (CRF) algorithm to build a Named Entity Recognition (NER) system based on a corpus of clinical Port
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c778c98057c71fd0edd5b5af0dc1ca5f
https://doi.org/10.5753/sbcas.2019.6269
https://doi.org/10.5753/sbcas.2019.6269
Autor:
Adalniza Moura Pucca da Silva, Oladimeji Farri, Lucas Emanuel Silva e Oliveira, Sadid A. Hasan, Caroline P. Gebeluca, Claudia Maria Cabral Moro
Publikováno v:
BIBM
Natural Language Processing and Machine Learning techniques can be used to automatically identify, extract and manipulate textual clinical data. Many of these methods are strongly dependent on annotated corpora that are very difficult to find in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ce8b84f05e0035a33cedd920d49a6dad
https://doi.org/10.1109/bibm.2017.8217805
https://doi.org/10.1109/bibm.2017.8217805
Autor:
Marc Cuggia, Vincent Claveau, Natalia Grabar, Claudia Maria Cabral Moro, Lucas Emanuel Silva e Oliveira, Guillaume Bouzillé
Publikováno v:
Artificial Intelligence in Medicine ISBN: 9783319597577
AIME
AIME 2017-16th Conference in Artificial Intelligence in Medecine
AIME 2017-16th Conference in Artificial Intelligence in Medecine, Jun 2017, Vienne, Austria. pp.203-208, ⟨10.1007/978-3-319-59758-4_22⟩
16th Conference on Artificial Intelligence in Medicine, AIME 2017
16th Conference on Artificial Intelligence in Medicine, AIME 2017, Jun 2017, Vienna, Austria. ⟨10.1007/978-3-319-59758-4_22⟩
AIME 2017-16th Conference in Artificial Intelligence in Medecine, Jun 2017, Vienne, Austria. Springer, 10259, pp.203-208, LNCS. 〈10.1007/978-3-319-59758-4_22〉
AIME
AIME 2017-16th Conference in Artificial Intelligence in Medecine
AIME 2017-16th Conference in Artificial Intelligence in Medecine, Jun 2017, Vienne, Austria. pp.203-208, ⟨10.1007/978-3-319-59758-4_22⟩
16th Conference on Artificial Intelligence in Medicine, AIME 2017
16th Conference on Artificial Intelligence in Medicine, AIME 2017, Jun 2017, Vienna, Austria. ⟨10.1007/978-3-319-59758-4_22⟩
AIME 2017-16th Conference in Artificial Intelligence in Medecine, Jun 2017, Vienne, Austria. Springer, 10259, pp.203-208, LNCS. 〈10.1007/978-3-319-59758-4_22〉
International audience; Clinical trials are fundamental for evaluating therapies and diagnosis techniques. Yet, recruitment of patients remains a real challenge. Eligibility criteria are related to terms but also to patient laboratory results usually
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf1cfb0e7654fb5863506c7f1a2feccb
https://doi.org/10.1007/978-3-319-59758-4_22
https://doi.org/10.1007/978-3-319-59758-4_22