Littérature scientifique sur le sujet « "Early Obesity Prediction" »
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Articles de revues sur le sujet ""Early Obesity Prediction""
Butler, Éadaoin M., José G. B. Derraik, Rachael W. Taylor et Wayne S. Cutfield. « Prediction Models for Early Childhood Obesity : Applicability and Existing Issues ». Hormone Research in Paediatrics 90, no 6 (2018) : 358–67. http://dx.doi.org/10.1159/000496563.
Texte intégralButler, Éadaoin M., José G. B. Derraik, Rachael W. Taylor et Wayne S. Cutfield. « Childhood obesity : how long should we wait to predict weight ? » Journal of Pediatric Endocrinology and Metabolism 31, no 5 (24 mai 2018) : 497–501. http://dx.doi.org/10.1515/jpem-2018-0110.
Texte intégralMukhopadhyay, S., A. Carroll, S. Downs et T. M. Dugan. « Machine Learning Techniques for Prediction of Early Childhood Obesity ». Applied Clinical Informatics 06, no 03 (2015) : 506–20. http://dx.doi.org/10.4338/aci-2015-03-ra-0036.
Texte intégralDatsenko, Natalya S., Igor O. Marinkin, Tat’yana M. Sokolova, Tat’yana V. Kiseleva et Anna V. Yakimova. « Early prediction of placental insufficiency in obese women ». V.F.Snegirev Archives of Obstetrics and Gynecology 8, no 1 (22 mars 2021) : 40–47. http://dx.doi.org/10.17816/2313-8726-2021-8-40-47.
Texte intégralKiseleva, O. I., E. V. Poverennaya, M. A. Pyatnitskiy, E. V. Ilgisonis, V. G. Zgoda, O. A. Plotnikova, K. K. Sharafetdinov et al. « DOES PROTEOMIC MIRROR REFLECT CLINICAL CHARACTERISTICS OF OBESITY ? » http://eng.biomos.ru/conference/articles.htm 1, no 19 (2021) : 129–30. http://dx.doi.org/10.37747/2312-640x-2021-19-129-130.
Texte intégralCheng, Erika R., Rai Steinhardt et Zina Ben Miled. « Predicting Childhood Obesity Using Machine Learning : Practical Considerations ». BioMedInformatics 2, no 1 (8 mars 2022) : 184–203. http://dx.doi.org/10.3390/biomedinformatics2010012.
Texte intégralRitz, Patrick, Robert Caiazzo, Guillaume Becouarn, Laurent Arnalsteen, Sandrine Andrieu, Philippe Topart et François Pattou. « Early prediction of failure to lose weight after obesity surgery ». Surgery for Obesity and Related Diseases 9, no 1 (janvier 2013) : 118–21. http://dx.doi.org/10.1016/j.soard.2011.10.022.
Texte intégralWahab, Rama J., Vincent W. V. Jaddoe et Romy Gaillard. « Prediction of Healthy Pregnancy Outcomes in Women with Overweight and Obesity : The Role of Maternal Early-Pregnancy Metabolites ». Metabolites 12, no 1 (24 décembre 2021) : 13. http://dx.doi.org/10.3390/metabo12010013.
Texte intégralGupta, Mehak, Thao-Ly T. Phan, H. Timothy Bunnell et Rahmatollah Beheshti. « Obesity Prediction with EHR Data : A Deep Learning Approach with Interpretable Elements ». ACM Transactions on Computing for Healthcare 3, no 3 (31 juillet 2022) : 1–19. http://dx.doi.org/10.1145/3506719.
Texte intégralMusa, Fati, Federick Basaky et Osaghae E.O. « Obesity prediction using machine learning techniques ». Journal of Applied Artificial Intelligence 3, no 1 (30 juin 2022) : 24–33. http://dx.doi.org/10.48185/jaai.v3i1.470.
Texte intégralThèses sur le sujet ""Early Obesity Prediction""
Shrestha, Pranav Nath. « Applying soft computing to early obesity prediction / ». Available to subscribers only, 2006. http://proquest.umi.com/pqdweb?did=1240705441&sid=9&Fmt=2&clientId=1509&RQT=309&VName=PQD.
Texte intégralMORANDI, Anita. « Estimation of newborn risk for child oradolescent obesity : lessons fromlongitudinal birth cohorts ». Doctoral thesis, 2012. http://hdl.handle.net/11562/388971.
Texte intégralChildhood obesity should be ideally prevented as early as possible during the child life. Assessing the inborn risk of obesity development would be the first step towards timely focused prevention. During my doctoral fellowship, I aimed at building clinical tools to predict the newborn risk for obesity development. To this purpose, I analyzed a unique prospective Northern Finland Birth Cohort 1986 (N=4032) (http://kelo.oulu.fi/NFBC) to assess the predictive value for child and adolescent obesity phenotypes of inborn traditional risk factors, like parental BMI, birth weight, maternal gestational weight, behaviour and social indicators, as well as of a genetic score issued from 39 BMI/obesity-associated polymorphisms. I also analysed a retrospective sample of 1,503 children aged 4-12 from Veneto, Italy, previously used in a survey assessing prevalence and risk factors of childhood obesity in the North of Italy, as a validation sample, in order to explore whether results issued from the NFBC1986 could be applied to a European paediatric cohort contemporary to the NFBC1986, with similar obesity prevalence (4%) but different cultural background. Finally, I analysed a prospective sample of 1032 children (7 years) from Massachusetts (United States) participating in the Project Viva (http://www.dacp.org/viva/index.html) as additional validation sample, in order to explore whether results issued from the NFBC1986 would remain valid when applied to a very recent U.S. child cohort, with higher obesity prevalence (8%) and very different cultural background. In the NFBC1986, I found a fair to good cumulative discrimination accuracy of traditional risk factors for the prediction of child obesity, adolescent obesity, and child obesity persistent into adolescence (0.75 < AUROCs < 0.85, p<0.001). The genetic score alone showed poor accuracy (0.55 < AUROCs < 0.60, p<0.001) for the prediction of obesity outcomes and, combined with clinical predictors, it only produced integrated discrimination improvements (IDI) < 1%. The version of the NFBC1986 equation for childhood obesity lacking gestational smoking and number of household members (not available in the Veneto dataset) had an AUROC = 0.70[0.63-0.77] (p < 0.001) when applied to the Veneto cohort versus an AUROC = 0.73[0.69 – 0.77] in the NFBC1986, with acceptable calibration accuracy (p for Hosmer-Lemeshow test > 0.05). The NFBC1986 equation for childhood obesity had an acceptable AUROC = 0.73[0.67-0.80] (p < 0.001) when applied to the project Viva children. However calibration in the Project Viva sample was not satisfactory (p for Hosmer-Lemeshow test = 0.02). The study I present here provides the first evidence that routinely available inborn variables may be combined into handy and cheap scores to estimate the newborn risk for obesity. An accurate estimation of the inborn risk to develop obesity may have important public health implications, being the basis for further efforts towards potential precocious prevention based on the “high risk approach”, i.e. focused on families of high risk newborns. The models I describe exploit variables usually easy to record retrospectively and strongly and consistently associated with obesity in several countries. Therefore, it is likely that reproduction/implementation or adaptation elsewhere can be successfully performed in a short time. This study also rules out the hypothesis that currently known polymorphisms may provide any useful contribution to accuracy of child obesity prediction.
Chapitres de livres sur le sujet ""Early Obesity Prediction""
Chatterjee, Kakali, Upendra Jha, Priya Kumari et Dhatri Chatterjee. « Early Prediction of Childhood Obesity Using Machine Learning Techniques ». Dans Lecture Notes in Electrical Engineering, 1431–40. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5341-7_109.
Texte intégralSingh, Balbir, et Hissam Tawfik. « Machine Learning Approach for the Early Prediction of the Risk of Overweight and Obesity in Young People ». Dans Lecture Notes in Computer Science, 523–35. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50423-6_39.
Texte intégralBanerjee, Samar. « Biomarkers in GDM, Role in Early Detection and Prevention ». Dans Gestational Diabetes Mellitus - New Developments. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.100563.
Texte intégralIndhumathi.M et V. D. Ambeth Kumar. « Future Prediction of Cardiovascular Disease Using Deep Learning Technique ». Dans Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210040.
Texte intégralIsong, Inyang A., Sowmya R. Rao, Marie-Abèle Bind, Mauricio Avendaño, Ichiro Kawachi et Tracy K. Richmond. « Racial and Ethnic Disparities in Early Childhood Obesity ». Dans Obesity : Stigma, Trends, and Interventions, 58–72. American Academy of Pediatrics, 2018. http://dx.doi.org/10.1542/9781610022781-racial.
Texte intégralBoussioutas, Alex, Stephen Fox, Iris Nagtegaal, Alexander Heriot, Jonathan Knowles, Michael Michael, Sam Ngan, Kathryn Field et John Zalcberg. « Colorectal cancer ». Dans Oxford Textbook of Oncology, 444–77. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199656103.003.0039.
Texte intégralBoussioutas, Alex, Stephen Fox, Iris Nagtegaal, Alexander Heriot, Jonathan Knowles, Michael Michael, Sam Ngan, Kathryn Field et John Zalcberg. « Colorectal cancer ». Dans Oxford Textbook of Oncology, 444–77. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199656103.003.0039_update_001.
Texte intégralDÍAZ-BURKE, Yolanda, Claudia Elena GONZÁLEZ-SANDOVAL, Rosario Lizette UVALLE-NAVARRO et Claudia Verónica MEDEROS-TORRES. « Pro-inflammatory cytokines : leptin and visfatin associated to obesity in young university students ». Dans CIERMMI Women in Science Medicine and Health Sciences Handbooks T-XIII, 78–88. ECORFAN-Mexico, S.C., 2021. http://dx.doi.org/10.35429/h.2021.13.78.88.
Texte intégralActes de conférences sur le sujet ""Early Obesity Prediction""
Ziauddeen, N., PJ Roderick, G. Santorelli, J. Wright et NA Alwan. « OP55 Childhood overweight and obesity at the start of primary school : external validation of pregnancy and early-life prediction models ». Dans Society for Social Medicine and Population Health Annual Scientific Meeting 2020, Hosted online by the Society for Social Medicine & Population Health and University of Cambridge Public Health, 9–11 September 2020. BMJ Publishing Group Ltd, 2020. http://dx.doi.org/10.1136/jech-2020-ssmabstracts.54.
Texte intégralPang, Xueqin, Christopher B. Forrest, Felice Le-Scherban et Aaron J. Masino. « Understanding Early Childhood Obesity via Interpretation of Machine Learning Model Predictions ». Dans 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00235.
Texte intégralAzzeh, Sara, Omar Alhussain et Sara Abu Samra. « A Mathematical Model to Decrease Obesity in the UAE ». Dans ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65871.
Texte intégralZiauddeen, N., S. Wilding, PJ Roderick, NS Macklon et NA Alwan. « OP38 Predicting the risk of childhood overweight and obesity at 4–5 years using pregnancy and early life healthcare data ». Dans Society for Social Medicine and Population Health and International Epidemiology Association European Congress Annual Scientific Meeting 2019, Hosted by the Society for Social Medicine & Population Health and International Epidemiology Association (IEA), School of Public Health, University College Cork, Cork, Ireland, 4–6 September 2019. BMJ Publishing Group Ltd, 2019. http://dx.doi.org/10.1136/jech-2019-ssmabstracts.38.
Texte intégralZiauddeen, Nida, Paul J. Roderick et Nisreen A. Alwan. « OP19 Predicting the risk of childhood overweight and obesity at 10–11 years using healthcare data from pregnancy and early life* ». Dans Society for Social Medicine Annual Scientific Meeting Abstracts. BMJ Publishing Group Ltd, 2021. http://dx.doi.org/10.1136/jech-2021-ssmabstracts.19.
Texte intégralRapports d'organisations sur le sujet ""Early Obesity Prediction""
Keshav, Dr Geetha, Dr Suwaibah Fatima Samer, Dr Salman Haroon et Dr Mohammed Abrar Hassan. TO STUDY THE CORRELATION OF BMI WITH ABO BLOOD GROUP AND CARDIOVASCULAR RISK AMONG MEDICAL STUDENTS. World Wide Journals, février 2023. http://dx.doi.org/10.36106/ijar/2405523.
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