Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by Crossref.
Reščič, Nina
Eftimov, Tome
Koroušić Seljak, Barbara
and
Luštrek, Mitja
2020.
Optimising an FFQ Using a Machine Learning Pipeline to Teach an Efficient Nutrient Intake Predictive Model.
Nutrients,
Vol. 12,
Issue. 12,
p.
3789.
Bodnar, Lisa M
Cartus, Abigail R
Kirkpatrick, Sharon I
Himes, Katherine P
Kennedy, Edward H
Simhan, Hyagriv N
Grobman, William A
Duffy, Jennifer Y
Silver, Robert M
Parry, Samuel
and
Naimi, Ashley I
2020.
Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes.
The American Journal of Clinical Nutrition,
Vol. 111,
Issue. 6,
p.
1235.
Correia, Maria Isabel T.D.
2020.
Nutrition in times of Covid-19, how to trust the deluge of scientific information.
Current Opinion in Clinical Nutrition & Metabolic Care,
Vol. 23,
Issue. 4,
p.
288.
Khorraminezhad, Leila
Leclercq, Mickael
Droit, Arnaud
Bilodeau, Jean-François
and
Rudkowska, Iwona
2020.
Statistical and Machine-Learning Analyses in Nutritional Genomics Studies.
Nutrients,
Vol. 12,
Issue. 10,
p.
3140.
Klimontov, V. V.
Berikov, V. B.
and
Saik, O. V.
2021.
Artificial intelligence in diabetology.
Diabetes mellitus,
Vol. 24,
Issue. 2,
p.
156.
Kim, Hyerim
Lim, Dong Hoon
and
Kim, Yoona
2021.
Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4–7th Korea National Health and Nutrition Examination Survey.
International Journal of Environmental Research and Public Health,
Vol. 18,
Issue. 11,
p.
5597.
Limketkai, Berkeley N.
Mauldin, Kasuen
Manitius, Natalie
Jalilian, Laleh
and
Salonen, Bradley R.
2021.
The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition.
Current Surgery Reports,
Vol. 9,
Issue. 7,
Sak, Jarosław
and
Suchodolska, Magdalena
2021.
Artificial Intelligence in Nutrients Science Research: A Review.
Nutrients,
Vol. 13,
Issue. 2,
p.
322.
Anagnostou, P.
Tasoulis, S.
Vrahatis, A. G.
Georgakopoulos, S.
Prina, M.
Ayuso-Mateos, J. L.
Bickenbach, J.
Bayes-Marin, I.
Caballero, F. F.
Egea-Cortés, L.
García-Esquinas, E.
Leonardi, M.
Scherbov, S.
Tamosiunas, A.
Galas, A.
Haro, J. M.
Sanchez-Niubo, A.
Plagianakos, V.
and
Panagiotakos, D.
2021.
Enhancing the Human Health Status Prediction: The ATHLOS Project.
Applied Artificial Intelligence,
Vol. 35,
Issue. 11,
p.
834.
Zhao, Junkang
Li, Zhiyao
Gao, Qian
Zhao, Haifeng
Chen, Shuting
Huang, Lun
Wang, Wenjie
and
Wang, Tong
2021.
A review of statistical methods for dietary pattern analysis.
Nutrition Journal,
Vol. 20,
Issue. 1,
Morgenstern, Jason D
Rosella, Laura C
Costa, Andrew P
de Souza, Russell J
and
Anderson, Laura N
2021.
Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology.
Advances in Nutrition,
Vol. 12,
Issue. 3,
p.
621.
Livingstone, Katherine M.
Ramos-Lopez, Omar
Pérusse, Louis
Kato, Hisanori
Ordovas, Jose M.
and
Martínez, J. Alfredo
2022.
Reprint of: Precision nutrition: A review of current approaches and future endeavors.
Trends in Food Science & Technology,
Vol. 130,
Issue. ,
p.
51.
Mazidi, Mohsen
Webb, Richard J.
George, Elena S.
Shekoohi, Niloofar
Lovegrove, Julie A.
and
Davies, Ian G.
2022.
Nutrient patterns are associated with discordant apoB and LDL: a population-based analysis.
British Journal of Nutrition,
Vol. 128,
Issue. 4,
p.
712.
Silva, Vanderlei Carneiro
Gorgulho, Bartira
Marchioni, Dirce Maria
Araujo, Tânia Aparecida de
Santos, Itamar de Souza
Lotufo, Paulo Andrade
and
Benseñor, Isabela Martins
2022.
Clustering analysis and machine learning algorithms in the prediction of dietary patterns: Cross‐sectional results of the Brazilian Longitudinal Study of Adult Health (ELSA‐Brasil).
Journal of Human Nutrition and Dietetics,
Vol. 35,
Issue. 5,
p.
883.
Livingstone, Katherine M.
Ramos-Lopez, Omar
Pérusse, Louis
Kato, Hisanori
Ordovas, Jose M.
and
Martínez, J. Alfredo
2022.
Precision nutrition: A review of current approaches and future endeavors.
Trends in Food Science & Technology,
Vol. 128,
Issue. ,
p.
253.
Kim, Hyerim
Hwang, Seunghyeon
Lee, Suwon
and
Kim, Yoona
2022.
Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm.
International Journal of Environmental Research and Public Health,
Vol. 19,
Issue. 22,
p.
15301.
Côté, Mélina
and
Lamarche, Benoît
2022.
Artificial intelligence in nutrition research: perspectives on current and future applications.
Applied Physiology, Nutrition, and Metabolism,
Vol. 47,
Issue. 1,
p.
1.
Suri, Jasjit S.
Bhagawati, Mrinalini
Paul, Sudip
Protogeron, Athanasios
Sfikakis, Petros P.
Kitas, George D.
Khanna, Narendra N.
Ruzsa, Zoltan
Sharma, Aditya M.
Saxena, Sanjay
Faa, Gavino
Paraskevas, Kosmas I.
Laird, John R.
Johri, Amer M.
Saba, Luca
and
Kalra, Manudeep
2022.
Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review.
Computers in Biology and Medicine,
Vol. 142,
Issue. ,
p.
105204.
Alkhalaf, Mohammad
Yu, Ping
Shen, Jun
and
Deng, Chao
2022.
A review of the application of machine learning in adult obesity studies.
Applied Computing and Intelligence,
Vol. 2,
Issue. 1,
p.
32.
Morgenstern, Jason D.
Rosella, Laura C.
Costa, Andrew P.
and
Anderson, Laura N.
2022.
Development of machine learning prediction models to explore nutrients predictive of cardiovascular disease using Canadian linked population-based data.
Applied Physiology, Nutrition, and Metabolism,
Vol. 47,
Issue. 5,
p.
529.