2023
Hernández, G.; González-Briones, A.; Machado, J.; Chamoso, P.; Novais, P.
A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents Proceedings Article
Em: C., Bonsangue M. M. Anutariya (Ed.): pp. 300-313, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18650929, (cited By 0; Conference of 1st International Conference on Data Science and Artificial Intelligence, DSAI 2023 ; Conference Date: 27 November 2023 Through 29 November 2023; Conference Code:304419).
Resumo | Links | BibTeX | Etiquetas: Craniofacial; Craniofacial morphological growth; Deep cavity; Dental extraction; Machine learning approaches; Machine learning techniques; Machine-learning; Morphological patterns; Orthodontic treatments; Tooth extraction, Decision making; Dentistry; Learning algorithms; Machine learning, Extraction
@inproceedings{Hernández2023300,
title = {A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents},
author = {G. Hernández and A. González-Briones and J. Machado and P. Chamoso and P. Novais},
editor = {Bonsangue M. M. Anutariya C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177874390&doi=10.1007%2f978-981-99-7969-1_22&partnerID=40&md5=97e552640b738ab3e2668bc83b3e3e02},
doi = {10.1007/978-981-99-7969-1_22},
issn = {18650929},
year = {2023},
date = {2023-01-01},
journal = {Communications in Computer and Information Science},
volume = {1942 CCIS},
pages = {300-313},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {There may be multiple reasons for tooth extraction, such as deep cavities, an infection that has destroyed an important portion of the tooth or the bone that surrounds it, or for orthodontic reasons, such as the lack of space for all the teeth in the mouth. In the case of orthodontics, however, there is a relationship between tooth extraction and the craniofacial morphological pattern. The purpose of this study is to establish whether such a relationship exists in adolescents and to evaluate it and to serve as a tool to support medical decision making. Machine Learning techniques can now be applied to datasets to discover relationships between different variables. Thus, this study involves the application of a series of Machine Learning techniques to a dataset containing information on orthodontic tooth extraction in adolescents. It has been discovered that by following simple rules it is possible to identify the need of treatment in 98.7% of the cases, while the remaining can be regarded as “limited cases”, in which an expert’s opinion is necessary. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.},
note = {cited By 0; Conference of 1st International Conference on Data Science and Artificial Intelligence, DSAI 2023 ; Conference Date: 27 November 2023 Through 29 November 2023; Conference Code:304419},
keywords = {Craniofacial; Craniofacial morphological growth; Deep cavity; Dental extraction; Machine learning approaches; Machine learning techniques; Machine-learning; Morphological patterns; Orthodontic treatments; Tooth extraction, Decision making; Dentistry; Learning algorithms; Machine learning, Extraction},
pubstate = {published},
tppubtype = {inproceedings}
}
Vaz, L.; Peixoto, H.; Duarte, J.; Alvarez, C.; Machado, J.
Enhancing Clinical Management of Bariatric Surgery Using Business Intelligence Proceedings Article
Em: E., Shakshuki (Ed.): pp. 850-855, Elsevier B.V., 2023, ISSN: 18770509, (cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712).
Resumo | Links | BibTeX | Etiquetas: Bariatric surgery; Business Intelligence platform; Business-intelligence; Clinical management; Healthcare services; Knowledge extraction; Obesity; Patient care; Potential impacts; Surgical treatment, Extraction, Information analysis; Nutrition; Surgery
@inproceedings{Vaz2023850,
title = {Enhancing Clinical Management of Bariatric Surgery Using Business Intelligence},
author = {L. Vaz and H. Peixoto and J. Duarte and C. Alvarez and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164522803&doi=10.1016%2fj.procs.2023.03.114&partnerID=40&md5=33c5a7e1ee9afa79be7d332a07083b79},
doi = {10.1016/j.procs.2023.03.114},
issn = {18770509},
year = {2023},
date = {2023-01-01},
journal = {Procedia Computer Science},
volume = {220},
pages = {850-855},
publisher = {Elsevier B.V.},
abstract = {There is a problem with collecting information in healthcare services as it is scattered among various sources. This leads to potential impact on patient care focus. To address this issue, a Business Intelligence platform was developed and implemented at the Centre for Surgical Treatment of Obesity at Centro Hospitalar do Tâmega e Sousa. The platform developed enables knowledge extraction and aids healthcare professionals to easily access helpful information and perform better decisions, specifically in regards to the growing global concern of obesity and the increasing prevalence of bariatric surgery. © 2023 Elsevier B.V.. All rights reserved.},
note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},
keywords = {Bariatric surgery; Business Intelligence platform; Business-intelligence; Clinical management; Healthcare services; Knowledge extraction; Obesity; Patient care; Potential impacts; Surgical treatment, Extraction, Information analysis; Nutrition; Surgery},
pubstate = {published},
tppubtype = {inproceedings}
}