SNOMED CT in a language isolate: an algorithm for a semiautomatic translation

Perez-de-Viñaspre, Olatz; Oronoz, Maite

doi:10.1186/1472-6947-15-s2-s5

Cited by 14 publications

(7 citation statements)

References 8 publications

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“…In a previous work, we developed a system to automatically translate English medical neoclassical compounds such as "glaucoma" or "meningitis" into Basque (Perez-de Viñaspre and Oronoz, 2015). This translation system is based on affix translation and a transliteration module was also implemented.…”

Section: System 2: Morphology Variability and Transliterationmentioning

confidence: 99%

“…State-of-the-art shows many approaches for transliteration in machine translation, most of them based on statistical methods (Deselaers et al, 2009;Habash, 2008;Hermjakob et al, 2008;Rama and Gali, 2009). In a previous work, we developed a system to automatically translate English medical neoclassical compounds such as "glaucoma" or "meningitis" into Basque (Perez-de Viñaspre and Oronoz, 2015). This translation system is based on affix translation and a transliteration module was also implemented.…”

Section: System 2: Morphology Variability and Transliterationmentioning

confidence: 99%

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IXA Biomedical Translation System at WMT16 Biomedical Translation Task

Perez-de-Viñaspre¹,

Labaka²

2016

Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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In this paper we present the system developed at the IXA NLP Group of the University of the Basque Country for the Biomedical Translation Task in the First Conference on Machine Translation (WMT16). For the adaptation of a statistical machine translation system to the biomedical domain, we developed three approaches based on a baseline system for English-Spanish and Spanish-English language pairs. The lack of terminology and the variation of the prominent sense of the words are the issues we have addressed on these approaches. The best of our systems reached the average of all the systems submitted in the challenge in most of the evaluation sets.

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Section: System 2: Morphology Variability and Transliterationmentioning

confidence: 99%

Section: System 2: Morphology Variability and Transliterationmentioning

confidence: 99%

IXA Biomedical Translation System at WMT16 Biomedical Translation Task

Perez-de-Viñaspre¹,

Labaka²

2016

Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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“…Moreover, due to the great versatility of ML techniques, they have been used in a wide variety of application areas, to discover hidden patterns in the datasets: identification and authentication of tequilas (Pérez-Caballero et al, 2017), wearable sensor data fusion (Kanjo, Younis & Sherkat, 2018), predicting the outcomes of organic reactions (Skoraczyñski et al, 2017), animal behaviour detection (Pons, Jaen & Catala, 2017) or to measure the visual complexity of images (Machado et al, 2015). In particular, ML techniques have proven to be able to uncover unimaginable relationships in very diverse fields of application, such as image or voice recognition, sentiment analysis or language translation (Li, Li & Wu, 2015;De Viñaspre & Oronoz, 2015).…”

Section: Introductionmentioning

confidence: 99%

Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques

Arceo-Vilas

Fernández-Lozano

Pita

et al. 2020

PeerJ Computer Science

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Food consumption patterns have undergone changes that in recent years have resulted in serious health problems. Studies based on the evaluation of the nutritional status have determined that the adoption of a food pattern-based primarily on a Mediterranean diet (MD) has a preventive role, as well as the ability to mitigate the negative effects of certain pathologies. A group of more than 500 adults aged over 40 years from our cohort in Northwestern Spain was surveyed. Under our experimental design, 10 experiments were run with four different machine-learning algorithms and the predictive factors most relevant to the adherence of a MD were identified. A feature selection approach was explored and under a null hypothesis test, it was concluded that only 16 measures were of relevance, suggesting the strength of this observational study. Our findings indicate that the following factors have the highest predictive value in terms of the degree of adherence to the MD: basal metabolic rate, mini nutritional assessment questionnaire total score, weight, height, bone density, waist-hip ratio, smoking habits, age, EDI-OD, circumference of the arm, activity metabolism, subscapular skinfold, subscapular circumference in cm, circumference of the waist, circumference of the calf and brachial area.

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“…Moreover, due to the great versatility of ML techniques, they have been used in a wide variety of application areas, to discover hidden patterns in the datasets: identification and authentication of tequilas (Pérez-Caballero et al, 2017), wearable sensor data fusion (Kanjo et al, 2018), predicting the outcomes of organic reactions (Skoraczyński et al, 2017), animal behaviour detection (Pons et al, 2017) or to measure the visual complexity of images (Machado et al, 2015). In particular, ML techniques have proven to be able to uncover unimaginable relationships in very diverse fields of application, such as image or voice recognition, sentiment analysis or language translation (Li et al, 2015;Perez-de Viñaspre and Oronoz, 2015).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #2 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.1)"

2020

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Food consumption patterns have undergone changes that in recent years have resulted in serious health problems. Studies based on the evaluation of the nutritional status have determined that the adoption of a food pattern-based primarily on a Mediterranean diet has a preventive role, as well as the ability to mitigate the negative effects of certain pathologies. A group of more than 500 adults, aged over 40 years from our cohort in Northwestern Spain was surveyed. Under our experimental design, ten experiments were run with four different machine-learning algorithms and the predictive factors most relevant to the adherence of a Mediterranean diet were identified. A feature selection approach was explored and under a null hypothesis test, it was concluded that only 16 measures were of relevance, suggesting the strength of this observational study. Our findings indicate that the following factors have the highest predictive value in terms of the degree of adherence to the Mediterranean diet: basal metabolic rate, mini nutritional assessment questionnaire total score, weight, height, bone density, waist-hip ratio, smoking habits, age, EDI-OD, circumference of the arm, activity metabolism, subscapular skinfold, subscapular circumference in cm, circumference of the waist, circumference of the calf and brachial area.

show abstract

SNOMED CT in a language isolate: an algorithm for a semiautomatic translation

Cited by 14 publications

References 8 publications

IXA Biomedical Translation System at WMT16 Biomedical Translation Task

IXA Biomedical Translation System at WMT16 Biomedical Translation Task

Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques

Peer Review #2 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.1)"

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