1. Bali RK. Clinical Knowledge Management: Opportunities and Challenges. . Hershey: Idea Group Inc (IGI); 2005. [DOI:10.4018/978-1-59140-300-5]

2. SIYAH B. The document in which the words "Artificial Intelligence" were written for the first time 2021. Available from: https://www.kaggle.com/general/246669.

3. libraries N. Artificial intelligence: Wikipedia; 2023. Available from: https://en.wikipedia.org/wiki/Artificial_intelligence.

4. Gholamhosseini L, Damroodi M. Evaluation of Data Mining Applications in the Health System. ajaums-jps. 2015;10(1):39-48.

5. Jahanbakhsh HMAHFAM. Application of Data Mining in Health. Health Information Management. 2011;9(2):297-304.

6. Pashaei; E, Fard MK, Chale A. An overview of data mining methods in the field of health. The second international conference and the third national conference on the application of new technologies in engineering sciences2015.

7. Bodnar LM, Cartus AR, Kirkpatrick SI, Himes KP, Kennedy EH, Simhan HN, et al. Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes. Am J Clin Nutr. 2020;111(6):1235-43. [DOI:10.1093/ajcn/nqaa027]

8. Bodnar LM, Kirkpatrick SI, Naimi AI. Machine learning can improve the development of evidence-based dietary guidelines. Public Health Nutr. 2022;25(9):2566-9. [DOI:10.1017/S1368980022001392]

9. Li H, Luo M, Zheng J, Luo J, Zeng R, Feng N, et al. An artificial neural network prediction model of congenital heart disease based on risk factors: A hospital-based case-control study. Medicine (Baltimore). 2017;96(6):e6090. [DOI:10.1097/MD.0000000000006090]

10. Mezgec S, Koroušić Seljak B. Deep Neural Networks for Image-Based Dietary Assessment. J Vis Exp. 2021(169). [DOI:10.3791/61906-v]

11. Monlezun DJ, Carr C, Niu T, Nordio F, DeValle N, Sarris L, et al. Meta-analysis and machine learning-augmented mixed effects cohort analysis of improved diets among 5847 medical trainees, providers and patients. Public Health Nutr. 2022;25(2):281-9. [DOI:10.1017/S1368980021002809]

12. Morgenstern JD, Rosella LC, Costa AP, Anderson LN. Development of machine learning prediction models to explore nutrients predictive of cardiovascular disease using Canadian linked population-based data. Appl Physiol Nutr Metab. 2022;47(5):529-46. [DOI:10.1139/apnm-2021-0502]

13. Rein D, Ternes P, Demin R, Gierke J, Helgason T, Schön C. Artificial intelligence identified peptides modulate inflammation in healthy adults. Food & Function. 2019;10(9):6030-41. [DOI:10.1039/C9FO01398A]

14. Silva VC, Gorgulho B, Marchioni DM, Araujo TA, Santos IS, Lotufo PA, et al. 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). J Hum Nutr Diet. 2022;35(5):883-94. [DOI:10.1111/jhn.12992]

15. Tao D, Yang P, Feng H. Utilization of text mining as a big data analysis tool for food science and nutrition. Comprehensive Reviews in Food Science and Food Safety. 2020;19(2):875-94. [DOI:10.1111/1541-4337.12540]

16. Kirk D, Catal C, Tekinerdogan B. Precision nutrition: A systematic literature review. Computers in biology and medicine. 2021;133:104365. [DOI:10.1016/j.compbiomed.2021.104365]

17. Matusheski NV, Caffrey A. Diets, nutrients, genes and the microbiome: recent advances in personalised nutrition. 2021;126(10):1489-97. [DOI:10.1017/S0007114521000374]

18. Miyazawa T, Hiratsuka Y, Toda M, Hatakeyama N, Ozawa H. Artificial intelligence in food science and nutrition: a narrative review. 2022;80(12):2288-300. [DOI:10.1093/nutrit/nuac033]

19. Oh YJ, Zhang J, Fang ML, Fukuoka Y. A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. The international journal of behavioral nutrition and physical activity. 2021;18(1):160. [DOI:10.1186/s12966-021-01224-6]

20. Mukhamediev RI, Popova Y, Kuchin Y, Zaitseva E, Kalimoldayev A, Symagulov A, et al. Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges. Mathematics. 2022;10(15). [DOI:10.3390/math10152552]

21. Santosh KC, Das N, Ghosh S. Chapter 2 - Deep learning: a review. In: Santosh KC, Das N, Ghosh S, editors. Deep Learning Models for Medical Imaging: Academic Press; 2022. p. 29-63. [DOI:10.1016/B978-0-12-823504-1.00012-X]

22. Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science. 2021;2(3):160. [DOI:10.1007/s42979-021-00592-x]

23. Robinson KG, Akins RE. Chapter 24 - Machine learning in epigenetic diseases. In: Tollefsbol TO, editor. Medical Epigenetics (Second Edition). 29: Academic Press; 2021. p. 513-25. [DOI:10.1016/B978-0-12-823928-5.00038-4]

24. Lugo Reyes SO. Chapter 21 - Artificial intelligence in precision health: Systems in practice. In: Barh D, editor. Artificial Intelligence in Precision Health: Academic Press; 2020. p. 499-519. [DOI:10.1016/B978-0-12-817133-2.00021-5]

25. El Bouchefry K, de Souza RS. Chapter 12 - Learning in Big Data: Introduction to Machine Learning. In: Škoda P, Adam F, editors. Knowledge Discovery in Big Data from Astronomy and Earth Observation: Elsevier; 2020. p. 225-49. [DOI:10.1016/B978-0-12-819154-5.00023-0]

26. Schneider P, Xhafa F. Chapter 8 - Machine learning: ML for eHealth systems. In: Schneider P, Xhafa F, editors. Anomaly Detection and Complex Event Processing over IoT Data Streams: Academic Press; 2022. p. 149-91. [DOI:10.1016/B978-0-12-823818-9.00019-5]

27. Kaelbling LP, Littman, M.L., and Moore, A.W. Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research. 1996;4:237-85. [DOI:10.1613/jair.301]

28. Hearty AP, Gibney MJ. Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees. Am J Clin Nutr. 2008;88(6):1632-42. [DOI:10.3945/ajcn.2008.26619]

29. Lazarou C, Karaolis M, Matalas AL, Panagiotakos DB. Dietary patterns analysis using data mining method. An application to data from the CYKIDS study. Comput Methods Programs Biomed. 2012;108(2):706-14. [DOI:10.1016/j.cmpb.2011.12.011]

30. Kastorini CM, Papadakis G, Milionis HJ, Kalantzi K, Puddu PE, Nikolaou V, et al. Comparative analysis of a-priori and a-posteriori dietary patterns using state-of-the-art classification algorithms: a case/case-control study. Artif Intell Med. 2013;59(3):175-83. [DOI:10.1016/j.artmed.2013.08.005]

31. Biesbroek S, van der A DL, Brosens MCC, Beulens JWJ, Verschuren WMM, van der Schouw YT, et al. Identifying cardiovascular risk factor-related dietary patterns with reduced rank regression and random forest in the EPIC-NL cohort12. The American Journal of Clinical Nutrition. 2015;102(1):146-54. [DOI:10.3945/ajcn.114.092288]

32. Panaretos D, Koloverou E, Dimopoulos AC, Kouli GM, Vamvakari M, Tzavelas G, et al. A comparison of statistical and machine-learning techniques in evaluating the association between dietary patterns and 10-year cardiometabolic risk (2002-2012): the ATTICA study. Br J Nutr. 2018;120(3):326-34. [DOI:10.1017/S0007114518001150]

33. Easton JF, Román Sicilia H, Stephens CR. Classification of diagnostic subcategories for obesity and diabetes based on eating patterns. Nutr Diet. 2019;76(1):104-9. [DOI:10.1111/1747-0080.12495]

34. Arceo-Vilas A, Fernandez-Lozano C, Pita S, Pértega-Díaz S, Pazos A. Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques. PeerJ Comput Sci. 2020;6:e287. [DOI:10.7717/peerj-cs.287]

35. Fu Y, Gou W, Hu W, Mao Y, Tian Y, Liang X, et al. Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort. BMC Medicine. 2020;18(1):184. [DOI:10.1186/s12916-020-01642-6]

36. He X, Matam BR, Bellary S, Ghosh G, Chattopadhyay AK. CHD Risk Minimization through Lifestyle Control: Machine Learning Gateway. Scientific Reports. 2020;10(1):4090. [DOI:10.1038/s41598-020-60786-w]

37. Yu EYW, Wesselius A, Sinhart C, Wolk A, Stern MC, Jiang X, et al. A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study. Br J Nutr. 2020;124(6):611-9. [DOI:10.1017/S0007114520001439]

38. Bôto JM, Marreiros A, Diogo P, Pinto E, Mateus MP. Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach. Public Health Nutr. 2021:1-13. [DOI:10.1017/S1368980021003293]

39. Soflaei SS, Shamsara E, Sahranavard T, Esmaily H, Moohebati M, Shabani N, et al. Dietary protein is the strong predictor of coronary artery disease; a data mining approach. Clin Nutr ESPEN. 2021;43:442-7. [DOI:10.1016/j.clnesp.2021.03.008]

40. Zhao Y, Naumova EN, Bobb JF, Claus Henn B, Singh GM. Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach. American Journal of Epidemiology. 2021;190(7):1353-65. [DOI:10.1093/aje/kwab004]

41. Choi I, Kim J, Kim WC. Dietary Pattern Extraction Using Natural Language Processing Techniques. Frontiers in Nutrition. 2022;9. [DOI:10.3389/fnut.2022.765794]

42. Mousavi H, Karandish M, Jamshidnezhad A, Hadianfard AM. Determining the effective factors in predicting diet adherence using an intelligent model. Scientific Reports. 2022;12(1):12340. [DOI:10.1038/s41598-022-16680-8]

43. Tatoli R, Lampignano L, Bortone I, Donghia R, Castellana F, Zupo R, et al. Dietary Patterns Associated with Diabetes in an Older Population from Southern Italy Using an Unsupervised Learning Approach. Sensors (Basel). 2022;22(6). [DOI:10.3390/s22062193]

44. Yang J, Yang A, Yeung S, Woo J, Lo K. Joint Associations of Food Groups with All-Cause and Cause-Specific Mortality in the Mr. OS and Ms. OS Study: A Prospective Cohort. Nutrients. 2022;14(19). [DOI:10.3390/nu14193915]

45. Limketkai BN, Hamideh M, Shah R, Sauk JS, Jaffe N. Dietary Patterns and Their Association With Symptoms Activity in Inflammatory Bowel Diseases. Inflamm Bowel Dis. 2022;28(11):1627-36. [DOI:10.1093/ibd/izab335]

46. Qarmiche N, El Kinany K, Otmani N, El Rhazi K, Chaoui NEH. Cluster analysis of dietary patterns associated with colorectal cancer derived from a Moroccan case-control study. BMJ Health Care Inform. 2023;30(1). [DOI:10.1136/bmjhci-2022-100710]

47. Taheri R, ZareMehrjardi F, Heidarzadeh-Esfahani N, Hughes JA, Reid RER, Borghei M, et al. Dietary intake of micronutrients are predictor of premenstrual syndrome, a machine learning method. Clin Nutr ESPEN. 2023;55:136-43. [DOI:10.1016/j.clnesp.2023.02.011]

48. Huang AA, Huang SY. Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations. Health science reports. 2023;6(10):e1635. [DOI:10.1002/hsr2.1635]

49. Martin-Morales A, Yamamoto M, Inoue M, Vu T, Dawadi R, Araki M. Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables. Nutrients. 2023;15(18). [DOI:10.3390/nu15183937]

50. Lampignano L, Tatoli R, Donghia R, Bortone I, Castellana F, Zupo R, et al. Nutritional patterns as machine learning predictors of liver health in a population of elderly subjects. Nutrition, metabolism, and cardiovascular diseases : NMCD. 2023;33(11):2233-41. [DOI:10.1016/j.numecd.2023.07.009]

51. Kirti K, Singh SK. Obesogenic diet and metabolic syndrome among adolescents in India: data-driven cluster analysis. BMC cardiovascular disorders. 2023;23(1):393. [DOI:10.1186/s12872-023-03429-y]

52. Zhao M, Wan J, Qin W, Huang X, Chen G, Zhao X. A machine learning-based diagnosis modelling of type 2 diabetes mellitus with environmental metal exposure. Computer methods and programs in biomedicine. 2023;235:107537. [DOI:10.1016/j.cmpb.2023.107537]

53. Li S, Hu X. Assessing the Risk of Prostate Cancer with Nutritional and Environmental Factors: A Cross-Sectional Study from National Health and Nutrition Examination Survey 2001-2010. Nutrition and cancer. 2023;75(5):1361-72. [DOI:10.1080/01635581.2023.2197687]