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- W3011682913 abstract "The food pattern is one of the modifiable factors for improving lifestyle and disease prevention. It is known that changes in diet have an effect on the evolution of chronic noncommunicable diseases (CNCD) of high prevalence, such as obesity, depression, anxiety, type 2 diabetes, and cardiovascular diseases. In order to prevent the CNCD, changing eating habits is strongly recommended. In addition, physical fitness, through systematized physical activities or that increase daily caloric expenditure, also contributes to the prevention of CNCD. Precision medicine, or precise health, is an approach for disease treatment and prevention that considers individual variability in genes, environment, and lifestyle. The applying of precision medicine has been broadly improved by the recent development of the large-scale biologic database, powerful methods for characterizing patients, and the use of high and smart technology. It is important to consider the computational tools for analyzing large data sets and, in this way, health-care providers will depend on electronic clinical decision support to quickly make appropriate treatment decisions. Computer systems that have a certain degree of intelligence and human/expert independence to infer about the preexisting data, in order to support the decision, could be useful, since the data generated require rapid and reliable analysis from a large number of variables. Among the available computational tools, artificial intelligence (AI) has gained more and more attention recently, since it is able to learn and model linear and nonlinear relationships between variables by constructing an input-output mapping such that hidden and extremely useful information for decision-making is revealed and interpreted. Although AI is not yet widely used in the areas of nutrition and fitness, it was found that the current technology available (information technology, several sensors, the use of nanotechnology and the advent of computers, IPhones, and smartphones) is favorable to the application of AI, since a large amount of data is collected by these technologies and, therefore, AI could be very useful in their mining. This chapter provides a discussion about the importance of nutrition and fitness for health and well-being; what is precision medicine, AI, precision nutrition, and precision fitness; how AI could help with precision nutrition and precision fitness; decision-making algorithm for nutritional meal planning/dietary menu planning; AI-based diet and supplements; AI used in genetic tests for precision nutrition and fitness; AI approach to nutritional meal planning for cancer, cardiovascular diseases, obesity, T2D patients; AI-based nutrition and fitness support systems and apps and some challenges and future perspectives." @default.
- W3011682913 created "2020-03-23" @default.
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- W3011682913 date "2020-01-01" @default.
- W3011682913 modified "2023-10-18" @default.
- W3011682913 title "Use of artificial intelligence in precision nutrition and fitness" @default.
- W3011682913 cites W1523943095 @default.
- W3011682913 cites W1764677737 @default.
- W3011682913 cites W1836486152 @default.
- W3011682913 cites W1900137331 @default.
- W3011682913 cites W1966427543 @default.
- W3011682913 cites W1977034798 @default.
- W3011682913 cites W1983110601 @default.
- W3011682913 cites W1983364832 @default.
- W3011682913 cites W1985104625 @default.
- W3011682913 cites W1990496546 @default.
- W3011682913 cites W1997273696 @default.
- W3011682913 cites W2010683549 @default.
- W3011682913 cites W2019528074 @default.
- W3011682913 cites W2019933371 @default.
- W3011682913 cites W2020724351 @default.
- W3011682913 cites W2021015732 @default.
- W3011682913 cites W2040184588 @default.
- W3011682913 cites W2040630171 @default.
- W3011682913 cites W2049027073 @default.
- W3011682913 cites W205954197 @default.
- W3011682913 cites W2060427373 @default.
- W3011682913 cites W2060449205 @default.
- W3011682913 cites W2081184698 @default.
- W3011682913 cites W2098386479 @default.
- W3011682913 cites W2107793190 @default.
- W3011682913 cites W2107975513 @default.
- W3011682913 cites W2124047443 @default.
- W3011682913 cites W2136238059 @default.
- W3011682913 cites W2166136857 @default.
- W3011682913 cites W2195311529 @default.
- W3011682913 cites W2205739583 @default.
- W3011682913 cites W2219995598 @default.
- W3011682913 cites W2261075337 @default.
- W3011682913 cites W2276633224 @default.
- W3011682913 cites W2279159495 @default.
- W3011682913 cites W2284544053 @default.
- W3011682913 cites W2291240878 @default.
- W3011682913 cites W2296522110 @default.
- W3011682913 cites W2398157737 @default.
- W3011682913 cites W2416285961 @default.
- W3011682913 cites W2472618036 @default.
- W3011682913 cites W2475704187 @default.
- W3011682913 cites W2479066671 @default.
- W3011682913 cites W2487654732 @default.
- W3011682913 cites W2512726309 @default.
- W3011682913 cites W2521731940 @default.
- W3011682913 cites W2546565370 @default.
- W3011682913 cites W2546822066 @default.
- W3011682913 cites W2549793983 @default.
- W3011682913 cites W2555327240 @default.
- W3011682913 cites W2555441663 @default.
- W3011682913 cites W2601594502 @default.
- W3011682913 cites W2605708433 @default.
- W3011682913 cites W2606905023 @default.
- W3011682913 cites W2616609740 @default.
- W3011682913 cites W2625824506 @default.
- W3011682913 cites W2637183865 @default.
- W3011682913 cites W2643943583 @default.
- W3011682913 cites W2646738377 @default.
- W3011682913 cites W2679983068 @default.
- W3011682913 cites W2709686885 @default.
- W3011682913 cites W2748986733 @default.
- W3011682913 cites W2749806135 @default.
- W3011682913 cites W2750176809 @default.
- W3011682913 cites W2753014607 @default.
- W3011682913 cites W2754977927 @default.
- W3011682913 cites W2756415582 @default.
- W3011682913 cites W2768472142 @default.
- W3011682913 cites W2771763805 @default.
- W3011682913 cites W2784295971 @default.
- W3011682913 cites W2786410355 @default.
- W3011682913 cites W2793532525 @default.
- W3011682913 cites W2801051483 @default.
- W3011682913 cites W2807415780 @default.
- W3011682913 cites W2810952583 @default.
- W3011682913 cites W4210559565 @default.
- W3011682913 doi "https://doi.org/10.1016/b978-0-12-817133-2.00020-3" @default.
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