人工智能在糖尿病诊疗中的研究进展

doi:10.3969/j.issn.1003-9198.2023.09.005

[1] GAUTIER T, ZIEGLER L B, GERBER M S, et al. Artificial intelligence and diabetes technology: a review [J]. Metabolism, 2021, 124: 154872.
[2] WEI Z, WANG K, QU H Q, et al. From disease association to risk assessment: an optimistic view from genome-wide association studies ontype 1 diabetes [J]. PLoS Genet, 2009, 5(10): e1000678.
[3] KAZEROUNI F, BAYANI A, ASADI F, et al. Type 2 diabetes mellitus prediction using data mining algorithms based on the long-noncoding RNAs expression: a comparison of four data mining approaches [J]. BMC Bioinformatics, 2020, 21(1): 372.
[4] MENG X H, HUANG Y X, RAO D P, et al. Comparison of three data mining models for predicting diabetes or prediabetes by risk factors [J]. Kaohsiung J Med Sci, 2013, 29(2): 93-99.
[5] 侯玉梅, 朱亚楠, 朱立春, 等. 决策树模型在2型糖尿病患病风险预测中的应用 [J]. 中国卫生统计, 2016, 33(6): 976-978,982.
[6] 李娟, 吴疆, 卢莉, 等. 基于支持向量机建立环境和遗传因素对2型糖尿病的预测模型 [J]. 中华疾病控制杂志, 2012, 16(2): 171-175.
[7] SELVIN E. Are there clinical implications of racial differences in HbA1c? A difference, to be a difference, must make a difference [J]. Diabetes Care, 2016, 39(8): 1462-1467.
[8] BAO Y, MA X, LI H, et al. Glycated haemoglobin A1c for diagnosing diabetes in Chinese population: cross sectional epidemiological survey [J]. BMJ, 2010, 340:c2249.
[9] 中国医师协会内分泌代谢科医师分会, 国家代谢性疾病临床医学研究中心. 糖尿病分型诊断中国专家共识 [J]. 中华糖尿病杂志, 2022, 14(2): 120-139.
[10] CHUNG W K, ERION K, FLOREZ J C, et al. Precision medicine in diabetes: a consensus report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) [J]. Diabetes Care, 2020, 43(7): 1617-1635.
[11] ZHENG T, XIE W, XU L, et al. A machine learning-based framework to identify type 2 diabetes through electronic health records [J].Int J Med Inform, 2017, 97: 120-127.
[12] GUEVARA E, TORRES-GALVÁN J C, RAMÍREZ-ELÍAS M G, et al. Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools [J].Biomed Opt Express, 2018, 9(10): 4998-5010.
[13] IPP E, LILJENQUIST D, BODE B, et al. Pivotal evaluation of an artificial intelligence system for autonomous detection of referrable and vision-threatening diabetic retinopathy [J]. JAMA Netw Open, 2021, 4(11): e2134254.
[14] VERBRAAK F D, ABRAMOFF M D, BAUSCH G C F, et al. Diagnostic accuracy of a device for the automated detection of diabetic retinopathy in a primary care setting [J]. Diabetes Care, 2019, 42(4): 651-656.
[15] DAI L, WU L, LI H, et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum [J]. Nat Commun, 2021, 12(1): 3242.
[16] LIN S, MA Y, XU Y, et al. Artificial intelligence in community-based diabetic retinopathy telemedicine screening in urban China: cost-effectiveness and cost-utility analyses with real-world data [J]. JMIR Public Health Surveill, 2023, 9: e41624.
[17] OZDEMIR F, ARI A, KILCIK M H, et al. Prediction of neuropathy, neuropathic pain and kinesiophobia in patients with type 2 diabetes and design of computerized clinical decision support systems by using artificial intelligence [J].Med Hypotheses, 2020, 143: 110070.
[18] RAHMANI KATIGARI M, AYATOLLAHI H, MALEK M, et al. Fuzzy expert system for diagnosing diabetic neuropathy [J]. World J Diabetes,2017,8(2):80-88.
[19] WILLIAMS B M, BORRONI D, LIU R, et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study [J]. Diabetologia, 2020, 63(2): 419-430.
[20] CRUZ-VEGA I, HERNANDEZ-CONTRERAS D, PEREGRINA-BARRETO H, et al. Deep learning classification for diabetic foot thermograms [J].Sensors: Basel,2020,20(6):1762.
[21] DREMIN V, MARCINKEVICS Z, ZHEREBTSOV E, et al. Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning [J].IEEE Trans Med Imaging, 2021, 40(4): 1207-1216.
[22] SCHÄFER Z, MATHISEN A, SVENDSEN K, et al. Toward machine-learning-based decision support in diabetes care: a risk stratification study on diabetic foot ulcer and amputation [J].Front Med: Lausanne, 2020, 7: 601602.
[23] SONG X, WAITMAN L R, YU A S, et al. Longitudinal risk prediction of chronic kidney disease in diabetic patients using a temporal-enhanced gradient boosting machine: retrospective cohort study [J]. JMIR Med Inform, 2020, 8(1):e15510.
[24] HÁSKOVÁ A, RADOVNICKá L, PETRUŽELKOVá L, et al. Real-time CGM is superior to flash glucose monitoring for glucose control in type 1 diabetes: the CORRIDA randomized controlled trial [J]. Diabetes Care, 2020, 43(11): 2744-2750.
[25] 计成, 代晶, 李林通,等. 基于人工智能系统的健康教育对2型糖尿病患者控制指标的影响[J]. 药学服务与研究, 2021, 21(1): 1-5.
[26] HAMON T, GAGNAYRE R. Improving knowledge of patient skills thanks to automatic analysis of online discussions [J]. Patient Educ Couns, 2013, 92(2): 197-204.
[27] TARUMI S, TAKEUCHI W, CHALKIDIS G, et al. Leveraging artificial intelligence to improve chronic disease care: methods and application to pharmacotherapy decision support for type-2 diabetes mellitus [J].Methods Inf Med, 2021, 60(S1): e32-e43.
[28] NIMRI R, BATTELINO T, LAFFEL L M, et al. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes [J].Nat Med,2020,26(9):1380-1384.
[29] KARIYAWASAM D, MORIN C, CASTEELS K, et al. Hybrid closed-loop insulin delivery versus sensor-augmented pump therapy in children aged 6-12 years: a randomised, controlled, cross-over, non-inferiority trial [J]. Lancet Digit Health, 2022, 4(3): e158-e168.
[30] BLOCK G, AZAR K M, ROMANELLI R J, et al. Diabetes prevention and weight loss with a fully automated behavioral intervention by Email, Web, and Mobile Phone: a randomized controlled trial among persons with prediabetes [J]. J Med Internet Res, 2015, 17(10): e240.

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Recommended 10

[1]	WANG Su, ZHAO Yijing, WEI Chenmin, CHEN Kun, CAO Wen, WANG Kun, YANG Yu. Correlation of triglyceride-glucose index with low-density lipoprotein subtypes in elderly patients with type 2 diabetes mellitus [J]. Practical Geriatrics, 2024, 38(10): 1002-1006.
[2]	GU Chonghuai, XIANG Xuejun, ZHENG Yuanxi, QIAO Rui, LIN Song. Efficacy of dapagliflozin in elderly patients undergoing coronary intervention with type 2 diabetes mellitus and ejection fraction reduced heart failure [J]. Practical Geriatrics, 2024, 38(10): 1025-1029.
[3]	. [J]. Practical Geriatrics, 2024, 38(10): 1066-1069.
[4]	LIU Qianhui, YAO Zijun, HE Yuli, XU Yunfan, WU Jun. Effects of HbA1c level on cardiac structure and function in elderly patients with type 2 diabetes mellitus and chronic heart failure [J]. Practical Geriatrics, 2024, 38(5): 491-437.
[5]	LIU Tongjun, ZHAO Yajie, HU Jieling, HU Yang, LIANG Wei. Effect of glucose metabolic disorders on serum levels of activin-A [J]. Practical Geriatrics, 2024, 38(5): 495-437.
[6]	. [J]. Practical Geriatrics, 2024, 38(5): 521-437.
[7]	. [J]. Practical Geriatrics, 2024, 38(5): 529-437.
[8]	TANG Yuan, DENG Shufang, HE Yongmei, DAI Yuxuan, LUO Jing. The status of health literacy and correlation with frailty in elderly patients with type 2 diabetes mellitus [J]. Practical Geriatrics, 2024, 38(3): 255-259.
[9]	DAI Ling-li, YU Yun. Association of estimated glomerular filtration rate with insulin resistance and β-cell function in elderly patients with type 2 diabetes mellitus [J]. Practical Geriatrics, 2023, 37(12): 1219-1223.
[10]	FAN Ting-yong, YUAN Li, YANG Xiao-ling, CHEN Qian. Assessment effect of Caprini scale on venous thromboembolism in elderly patients with diabetes [J]. Practical Geriatrics, 2023, 37(12): 1233-1237.
[11]	ZHANG He-cheng, WEI Su. Correlation of serum ferritin level with diabetic foot in elderly patients with type 2 diabetes mellitus [J]. Practical Geriatrics, 2023, 37(11): 1111-1114.
[12]	HU Yan, WU Hai-di, WANG Xue-fei, LOU Qing-lin, YE Qing, GU Liu-bao, DAI Jing, SONG Xiao-bo. Correlation between diabetic foot risk score and urinary microalbumin/creatinine ratio in elderly patients with type 2 diabetes [J]. Practical Geriatrics, 2023, 37(11): 1134-1137.
[13]	. [J]. Practical Geriatrics, 2023, 37(11): 1170-1175.
[14]	FENG Hai-mei, WEI Xue-mei, XU Zhi-xin. Effect of glycemic control degree on cisatracurium-induced neuromuscular block in elderly diabetic patients undergoing general anesthesia during perioperative period [J]. Practical Geriatrics, 2023, 37(8): 848-852.
[15]	. [J]. Practical Geriatrics, 2023, 37(8): 865-872.