Topic Modeling for Automatically Identification of STEM Barriers
año: 2022 - tipo: Scopus
- https://doi.org/10.1007/978-3-031-03884-6_22 - Google Schoolar - Ver abstract
Exploratory Data Analysis on Cervical Cancer Diseases
año: 2022 - tipo: Scopus
- https://doi.org/10.1007/978-3-031-03884-6_32 - Google Schoolar - Ver abstract
Comparison of Clusters Algorithms to Identify Patterns in Information Related to Cervical Cancer
año: 2022 - tipo: Scopus
- https://doi.org/10.1007/978-3-031-04826-5_40 - Google Schoolar - Ver abstract
Topic identification from news blog in Spanish language
año: 2022 - tipo: Latindex Catálogo
- https://doi.org/ https://doi.org/10.33936/isrtic.v6i1.4514 - Google Schoolar - Ver abstract
Topic Identification from Spanish Unstructured Health Texts
año: 2021 - tipo: Scopus
- https://doi.org/10.1007/978-3-030-71503-8_27 - Google Schoolar - Ver abstract
Identification of drug relationships base on drugs codes extracted from informatics systems [Identificación de relaciones entre medicamentos en base a códigos extraídos de un sistema informático]
año: 2021 - tipo: Scopus
- https://www.scopus.com/record/display.uri?eid=2-s2.0-85105519940&origin=resultslist&sort=plf-t&src=s&sid=5d74215da3ecafe8f6590bb21b67055c&sot=b&sdt=b&sl=210&s=TITLE-ABS-KEY%28Identification+of+drug+relationships+base+on+drugs+codes+extracted+from+informatics+systems+%5bIdentificación+de+relaciones+entre+medicamentos+en+base+a+códigos+extra%C3%ADdos+de+un+sistema+informático%5d%29&relpos=0&citeCnt=0&searchTerm= - Google Schoolar - Ver abstract
Word embedding application for rendering and exploration of unstructured medical information [Aplicación de Word embedding para la representación y exploración de información médica no estructurada]
año: 2021 - tipo: Scopus
- Digital medical records store information that can describe a patient’s health. Word embedding allows textual information to be represented using low-dimensional dense vectors. These types of models capture the semantic and syntactic information of a corpus of information and therefore can be used to identify linguistic relationships between words. Considering the aforementioned, the objective of this work is to apply word embedding to represent medical documentation in digital format and identify similarities between certain medical terms. The results showed that the words most similar to obesity are: asthma, hypertension, diabetes, congestive heart failure, atherosclerotic cardiovascular disease, depression and sleep apnea, considered comorbidities of obesity. It was also found that there is a certain difference when finding similarities between two words, such as obesity and man, and between obesity and woman - Google Schoolar - Ver abstract
Data Mining in Electrical Distribution Networks: Optimal Location of Pilot Bus
año: 2021 - tipo: Scopus
- https://doi.org/10.1109/ETCM53643.2021.9590646 - Google Schoolar - Ver abstract
Artificial Intelligence for Cervical Cancer Analysis
año: 2021 - tipo: Scopus
- https://doi.org/https://doi.org/10.30534/ijatcse/2021/671032021 - Google Schoolar - Ver abstract
Using domain ontologies for text classification. A use case to classify computer science papers
año: 2020 - tipo: Scopus
- https://doi.org/10.1007/978-3-030-65384-2_13 - Google Schoolar - Ver abstract
Memorias X Investiga UTPL_Posters_2021
año: 2021 - tipo: Libros de Actas
Memorias IX INVESTIGA UTPL 2020
año: 2020 - tipo: Libros de Actas
Proceedings of the International Conference on Information Technology & Systems
año: 2018 - tipo: Libros de Actas
Tendencias y avances en los sistemas y tecnologías de la información
año: 2018 - tipo: Libros de Actas
Frontiers in Education Conference Proceedings
año: 2015 - tipo: Libros de Actas
Ambient Assisted Living and Daily Activities
año: 2014 - tipo: Científico
Actas del IV Congreso Internacional sobre Aplicación de Tecnologías de la Información y Comunicaciones Avanzadas (ATICA2012)
año: 2012 - tipo: Libros de Actas
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