Use of artificial intelligence in precision nutrition and fitness

Lopes, María Helena Baena de Moraes; Ferreira, Danton Diego; Ferreira, Ana Cláudia Barbosa Honório; Silva, Giuliano Roberto da; Caetano, Aletha Silva; Braz, Vitória Negri

doi:10.1016/b978-0-12-817133-2.00020-3

Cited by 27 publications

(15 citation statements)

References 76 publications

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“…Therefore, the quality of care has a great impact on the safety of care. A number of studies have confirmed that the quality of care is an important factor in the health of patients, and as a special group, it is particularly important to improve the quality of care [12].…”

Section: Introductionmentioning

confidence: 99%

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Zhao

2022

Wireless Communications and Mobile Computing

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Nowadays, people are paying greater attention to the concept of childcare, and the healthy development of children is humanity’s hope for the future. The newborn stage is the most critical stage of a child’s development, and learning and mastering neonatal care is the first important lesson for parents. To meet the needs of today’s learners and to break the traditional teaching model, this paper develops a set of interactive newborn nursing knowledge system that composes the content of trivial nursing knowledge, reconstructs the knowledge structure system, and provides new teaching methods such as text browsing, 3D animation presentation, picture presentation, video presentation, interactive games, and tests for newborns. Text browsing, 3D animation, image demonstration, video demonstration, and interactive games and tests, among other new teaching methods, give a new teaching mode and method for neonatal nursing education. The study included 158 infants from local hospital. The study was divided into two groups, each with 80 cases: a control group (conventional care) and a study group (quality care management). The outcomes were contrasted and compared. The results revealed that the study group had a lower incidence of nursing defects than the control group, and the difference was statistically significant ( P 0.05 ); the study group had a lower incidence of diseases than the control group, and the difference was statistically significant ( P < 0.05 ). The study goes over the complete process of incorporating the instructional display into the interactive newborn care knowledge system. Multimedia techniques and applications were researched throughout the design and production process in order to blend intrinsic knowledge material with digital technology to produce new educational ideas.

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Section: Introductionmentioning

confidence: 99%

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Zhao

2022

Wireless Communications and Mobile Computing

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“…First evidence suggests that even for individuals at high genetic CAD risk and with pre-existing non-modifiable risk factors (age, sex, positive family history) adherence to a healthy lifestyle could be associated with an almost 50% lower relative risk of CAD (Khera et al, 2017 ; Dimovski et al, 2019 ), indicating that the inclusion of dietary factors can substantially improve CAD risk prediction, as compared to standard Cox models without these additional variables (Rigdon and Basu, 2019 ; Ho et al, 2020 ). With the advent of biosensors and wearable health technology connected to mobile apps, large-scale longitudinal food diaries and images of meals consumed are increasingly becoming available and are even being integrated within electronic health records (Verma et al, 2018 ; Dinh-Le et al, 2019 ; Moraes Lopes et al, 2020 ), whereas further advances in and rapidly decreasing costs of next generation sequencing generate increasing data volumes describing the human gut microbiome qualitative and quantitative composition and function (Eetemadi et al, 2020 ), thus providing valuable sources of data for integration in the context of personalized diet recommendation systems (Eetemadi et al, 2020 ), which could be further integrated into clinical decision support systems for improved CAD risk predictions. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD (Pallazola et al, 2019 ), indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses to diet (Hughes et al, 2019 ), and that interactions between diet and other factors need to be considered (Qi, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, the recent re-emergence of advanced computational data-driven technologies such as Artificial Intelligence (AI)/Machine Learning (ML) approaches are opening intriguing perspectivesfor the integration of omics data (genetic variations, gut microbiome) with additional clinical (Reel et al, 2021 ) and environmental/lifestyle and the development personalized CAD diagnostics tools (Alizadehsani et al, 2019 ). AI/ML represent automated approaches that are adaptive and able to capture large and heterogeneous matrices of data extracting meaningful patterns and identifying both linear and non-linear relationships between these high-dimensional input variables and the outcomes (Alaa et al, 2019 ; Rigdon and Basu, 2019 ; Bodnar et al, 2020 ; Moraes Lopes et al, 2020 ). Especially, Deep Learning (DL) approaches, hold a great promise for future progress due to its capabilities to learn from input raw data, instead of using hand-crafted features that require domain expertise (Ching et al, 2018 ; Solares et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease

et al. 2022

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Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.

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“…With increasingly available multidimensional big data and machine learning (ML) techniques, such precision nutrition approaches are needed to understand nutritional aetiopathogenesis of disease and to develop tailored programs [ 13 ]. Presently, ML is sparingly used in nutrition research [ 14 ], despite its promise and broadening applications in other areas of research including type 2 diabetes [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking

et al. 2021

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Objectives Using a nationally-representative, cross-sectional cohort, we examined nutritional markers of undiagnosed type 2 diabetes in adults via machine learning. Methods A total of 16429 men and non-pregnant women ≥ 20 years of age were analysed from five consecutive cycles of the National Health and Nutrition Examination Survey. Cohorts from years 2013–2016 (n = 6673) was used for external validation. Undiagnosed type 2 diabetes was determined by a negative response to the question “Have you ever been told by a doctor that you have diabetes?” and a positive glycaemic response to one or more of the three diagnostic tests (HbA1c > 6.4% or FPG >125 mg/dl or 2-hr post-OGTT glucose > 200mg/dl). Following comprehensive literature search, 114 potential nutritional markers were modelled with 13 behavioural and 12 socio-economic variables. We tested three machine learning algorithms on original and resampled training datasets built using three resampling methods. From this, the derived 12 predictive models were validated on internal- and external validation cohorts. Magnitudes of associations were gauged through odds ratios in logistic models and variable importance in others. Models were benchmarked against the ADA diabetes risk test. Results The prevalence of undiagnosed type 2 diabetes was 5.26%. Four best-performing models (AUROC range: 74.9%-75.7%) classified 39 markers of undiagnosed type 2 diabetes; 28 via one or more of the three best-performing non-linear/ensemble models and 11 uniquely by the logistic model. They comprised 14 nutrient-based, 12 anthropometry-based, 9 socio-behavioural, and 4 diet-associated markers. AUROC of all models were on a par with ADA diabetes risk test on both internal and external validation cohorts (p>0.05). Conclusions Models performed comparably to the chosen benchmark. Novel behavioural markers such as the number of meals not prepared from home were revealed. This approach may be useful in nutritional epidemiology to unravel new associations with type 2 diabetes.

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Use of artificial intelligence in precision nutrition and fitness

Cited by 27 publications

References 76 publications

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease

Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking

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