[1] |
Businesswire. 10-Key-Trends-Food-Nutrition-Health-2019[EB/OL]. (2019-07-25) [2022-05-13].
URL
|
[2] |
刘琰,陈伟. 精准营养新定义:理念与落实[J]. 中华预防医学杂志,2022, 56(2): 151-153.
|
[3] |
何俏,时景璞. 临床真实世界研究中的实验性研究设计[J]. 中华流行病学杂志,2018, 39(4): 519-523.
|
[4] |
Zeevi D, Korem T, Zmora N, et al. Personalized Nutrition by Prediction of Glycemic Responses[J]. Cell, 2015, 163(5): 1079-1094.
|
[5] |
Ferguson LR, De Caterina R, Görman U, et al. Guide and Position of the International Society of Nutrigenetics/Nutrigenomics on Personalised Nutrition: Part 1 - Fields of Precision Nutrition[J]. J Nutrigenet Nutrigenomics, 2016, 9(1): 12-27.
|
[6] |
马国耀,孙勇韬,马玉玲. 数据采集模板化技术在医疗大数据集成建设中的应用[J]. 中国卫生信息管理杂志,2016, 13(4): 414-416.
|
[7] |
周光华,李岳峰. 数据挖掘技术在卫生统计信息工作中的应用研究[J]. 中国卫生信息管理杂志,2012, 9(6): 82-86.
|
[8] |
Wu S, Roberts K, Datta S, et al. Deep learning in clinical natural language processing: a methodical review[J]. J Am Med Inform Assoc, 2020, 27(3): 457-470.
|
[9] |
Chen L, Song L, Shao Y, et al. Using natural language processing to extract clinically useful information from Chinese electronic medical records[J]. Int J Med Inform, 2019, 124: 6-12.
|
[10] |
Patel TA, Puppala M, Ogunti RO, et al. Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods[J]. Cancer, 2017, 123(1): 114-121.
|
[11] |
阮彤,高炬,冯东雷,等. 基于电子病历的临床医疗大数据挖掘流程与方法[J]. 大数据,2017, 3(5): 83-98.
|
[12] |
Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research[J]. J Am Med Inform Assoc, 2013, 20(1): 144-151.
|
[13] |
熊兴江,马敬东,徐承中. 电子健康档案数据质量评估与治理的综述研究[J]. 中国卫生信息管理杂志,2018, 15(6): 637-642.
|
[14] |
王宇. 基于机器学习建立创伤代谢组学快速精准分析技术的研究[D]. 成都:电子科技大学,2019.
|
[15] |
Lei C, Wang Y, Zhao J, et al. A patient specific forecasting model for human albumin based on deep neural networks[J]. Compute Methods Programs Biomed, 2020, 196: 105555.
|
[16] |
Tchagna Kouanou A, Mih Attia T, Feudjio C, et al. An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection[J]. J Healthc Eng, 2021, 2021: 4733167.
|
[17] |
Westerhuis JA, van Velzen EJ, Hoefsloot HC, et al. Multivariate paired data analysis: multilevel PLSDA versus OPLSDA[J]. Metabolomics, 2010, 6(1): 119-128.
|
[18] |
Zhang F, Tapera TM, Gou J. Application of a new dietary pattern analysis method in nutritional epidemiology[J]. BMC Med Res Methodol, 2018, 18(1): 119.
|
[19] |
Kirk D, Catal C, Tekinerdogan B. Precision nutrition: A systematic literature review[J]. Comput Biol Med, 2021, 133: 104365.
|
[20] |
Jones P, Mirkes EM, Yates T, et al. Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data[J]. Sensors (Basel), 2019, 19(20): 4504.
|
[21] |
Deng HF, Sun MW, Wang Y, et al. Evaluating machine learning models for sepsis prediction: A systematic review of methodologies[J]. iScience, 2021, 25(1): 103651.
|
[22] |
Khorraminezhad L, Leclercq M, Droit A, et al. Statistical and Machine-Learning Analyses in Nutritional Genomics Studies[J]. Nutrients, 2020, 12(10): 3140.
|
[23] |
Lee YC, Christensen JJ, Parnell LD, et al. Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions[J]. Front Genet, 2022, 12: 783845.
|
[24] |
Li ZJ, Chen W, Jiang H, et al. Effects of Postoperative Parenteral Nutrition Enhanced by Multivitamin on Metabolic Phenotype in Postoperative Gastric Cancer Patients[J]. Mol Nutr Food Res, 2018, 62(12): e1700757.
|
[25] |
Tan Q, Wang Y, Zhang G, et al. The metabolic effects of multi-trace elements on parenteral nutrition for critically ill pediatric patients: a randomized controlled trial and metabolomic research[J]. Transl Pediatr, 2021, 10(10): 2579-2593.
|
[26] |
Shen X, Wang C, Liang N, et al. Serum Metabolomics Identifies Dysregulated Pathways and Potential Metabolic Biomarkers for Hyperuricemia and Gout[J]. Arthritis Rheumatol, 2021, 73(9): 1738-1748.
|
[27] |
Ulaszewska MM, Weinert CH, Trimigno A, et al. Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies[J]. Mol Nutr Food Res, 2019, 63(1): e1800384.
|
[28] |
Fan J, Meng Q, Guo G, et al. Effects of early enteral nutrition supplemented with arginine on intestinal mucosal immunity in severely burned mice[J]. Clin Nutr, 2010, 29(1): 124-130.
|
[29] |
Xu D, Lu Q, Deitch EA. Elemental diet-induced bacterial translocation associated with systemic and intestinal immune suppression[J]. JPEN J Parenter Enteral Nutr, 1998, 22(1): 37-41.
|
[30] |
Okamoto K, Fukatsu K, Hashiguchi Y, et al. Lack of preoperative enteral nutrition reduces gut-associated lymphoid cell numbers in colon cancer patients: a possible mechanism underlying increased postoperative infectious complications during parenteral nutrition[J]. Ann Surg, 2013, 258(6): 1059-1064.
|
[31] |
Heneghan AF, Pierre JF, Tandee K, et al. Parenteral nutrition decreases paneth cell function and intestinal bactericidal activity while increasing susceptibility to bacterial enteroinvasion[J]. JPEN J Parenter Enteral Nutr, 2014, 38(7): 817-824.
|
[32] |
Danneskiold-Samsøe NB, Dias de Freitas Queiroz Barros H, Santos R, et al. Interplay between food and gut microbiota in health and disease[J]. Food Res Int, 2019, 115: 23-31.
|
[33] |
Wu H, Tremaroli V, Schmidt C, et al. The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study[J]. Cell Metab, 2020, 32(3): 379-390. e3.
|
[34] |
Gou W, Ling CW, He Y, et al. Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes[J]. Diabetes Care, 2021, 44(2): 358-366.
|
[35] |
Wang K, Zeng Q, Li KX, et al. Efficacy of probiotics or synbiotics for critically ill adult patients: a systematic review and meta-analysis of randomized controlled trials[J]. Burns Trauma, 2022, 10: tkac004.
|
[36] |
吴桐,王鸿超,陆文伟,等. 肥胖人群肠道菌群特征分析及机器学习模型[J]. 微生物学通报,2020, 47(12): 4328-4337.
|
[37] |
Wang S, Zhang L, Wang D, et al. Gut Microbiota Composition is Associated with Responses to Peanut Int ervention in Multiple Parameters Among Adults with Metabolic Syndrome Risk[J]. Molr Nutr Food Res, 2021: 2001051.
|
[38] |
Sak J, Suchodolska M. Artificial Intelligence in Nutrients Science Research: A Review[J]. Nutrients, 2021, 13(2): 322.
|
[39] |
Morgenstern JD, Rosella LC, Costa AP, et al. Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology[J]. Adv Nutr, 2021, 12(3): 621-631.
|
[40] |
Oliveira Chaves L, Gomes Domingos AL, Louzada Fernandes D, et al. Applicability of machine learning techniques in food intake assessment: A systematic review[J]. Crit Rev Food Sci Nutr, 2021, 29: 1-18.
|
[41] |
Morgenstern JD, Rosella LC, Costa AP, et al. Development of Machine Learning Prediction Models to Explore Nutrients Predictive of Cardiovascular Disease Using Canadian Linked Population-Based Data [J]. Appl Physiol Nutr Metab, 2022, 47(5): 529-546.
|