Sistema Experto Probabilístico basado en Redes Bayesianas para la predicción del cáncer de cuello uterino

Flores, Luis A.; Dueñas, Ana María Huayna

doi:10.15381/rpcs.v2i1.16360

Cited by 2 publications

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial Intelligence for Sexual, Reproductive and Maternal Health: A Scoping Review (Preprint)

Saban,

Zavala,

Rubinstein

et al. 2024

Preprint

View full text Add to dashboard Cite

BACKGROUND - OBJECTIVE To survey and analyze the applications of artificial intelligence tools in the field of sexual, reproductive and maternal health in Latin America and the Caribbean. METHODS A scoping review was conducted using the methodological framework proposed by Arksey and O'Malley and the PRISMA statement for Systematic Reviews and Meta-analyses, extension for Scoping Reviews. The search was carried out in PubMed, Scielo, Cochrane and Lilacs. RESULTS Of the 1,518 articles identified, 143 were included and analyzed. The review evidences the predominant focus of artificial intelligence for prenatal, childbirth, and postnatal care, as well as in the prevention, detection, and treatment of cancers of the reproductive organs. The tools mainly use machine learning and deep learning techniques, and are in different stages of development: 48% represent exploratory projects, 17% are operational tools but do not have a results report, and 35% are projects implemented in real contexts and provide evidence of the effectiveness of the tools used. CONCLUSIONS The need to diversify the applications of artificial intelligence in essential sexual, reproductive and maternal health services and ensure the inclusion of diverse populations to ensure equitable and effective access to them is highlighted CLINICALTRIAL

show abstract

Artificial Intelligence for Sexual, Reproductive and Maternal Health: A Scoping Review (Preprint)

Saban,

Zavala,

Rubinstein

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Selección de un método de aprendizaje automático para clasificar patrones biomarcadores de lesiones precancerosas de las cuerdas vocales

Siordia-Vásquez

Villagrán-Villegas

Patiño-Ortiz

et al. 2020

JPD

View full text Add to dashboard Cite

The National Survey on Drug, Alcohol and Tobacco, 2016-2017, notes that 15.6 million Mexicans are active smokers and, by 2030, expect the death of 8 million cancers of the larynx or lung. Therefore, the World Health Organization (WHO) recommends detecting precancerous lesions of the larynx. This is possible, as they are characterized by a biomarker pattern manifested by the alteration of the biomechanical interpretation of the vocal cords, regardless of the sex and age of the smoker. The goal of this article is to evaluate three machine learning methods: neural networks, Gaussian networks, and decision tree to determine the method that best solves the problem of detecting patterns of precancerous vocal cord injury biomarkers. It uses the WEKA tool and a knowledge bank, endorsed by NOM-012-SSA3-2012, with 250 patterns, and provided by the Luis Guillermo Ibarra National Institute of Rehabilitation, Ibarra. The performance of the methods was compared by ROC curves and confusion matrices, under the criteria established by ISO-5725. The decision tree the method that best responds to the detection of patterns of biomarkers of precancerous lesions of the vocal cords.

show abstract

Sistema Experto Probabilístico basado en Redes Bayesianas para la predicción del cáncer de cuello uterino

Cited by 2 publications

References 14 publications

Artificial Intelligence for Sexual, Reproductive and Maternal Health: A Scoping Review (Preprint)

Artificial Intelligence for Sexual, Reproductive and Maternal Health: A Scoping Review (Preprint)

Selección de un método de aprendizaje automático para clasificar patrones biomarcadores de lesiones precancerosas de las cuerdas vocales

Contact Info

Product

Resources

About