中华护理杂志 ›› 2023, Vol. 58 ›› Issue (9): 1063-1067.DOI: 10.3761/j.issn.0254-1769.2023.09.006
收稿日期:2022-09-15
出版日期:2023-05-10
发布日期:2023-05-10
*通讯作者:
冯素文,E-mail:fengsw@zju.edu.cn作者简介:徐雪芬:女,硕士,主管护师,护士长,E-mail:5613024@zju.edu.cn
基金资助:
XU Xuefen(
), WANG Hongyan, GUO Pingping, WANG Yulu, FENG Suwen(
)
Received:2022-09-15
Online:2023-05-10
Published:2023-05-10
摘要:
慢性病因其长期性、难治愈性成为威胁患者健康的重要公共卫生问题之一,人工智能为实现高效的慢性病健康管理提供了方法。该文对人工智能及其在慢性病健康管理领域中的应用现状进行综述,同时对应用中存在的问题进行总结并提出展望,以期为人工智能在慢性病健康管理中的应用提供借鉴。
徐雪芬, 王红燕, 郭萍萍, 王宇璐, 冯素文. 人工智能在慢性病患者健康管理中的应用进展[J]. 中华护理杂志, 2023, 58(9): 1063-1067.
XU Xuefen, WANG Hongyan, GUO Pingping, WANG Yulu, FENG Suwen. Progress on the application of artificial intelligence in chronic disease health management[J]. Chinese Journal of Nursing, 2023, 58(9): 1063-1067.
| [1] | 范利. 我国老年人慢性病防控迫在眉睫[J]. 中国临床保健杂志, 2019, 22(4):433-434. |
| Fan L. The prevention and control of chronic diseases in the elderly is urgent in China[J]. Chin J Clin Healthc, 2019, 22(4):433-434. | |
| [2] | Antón Má, Ordieres-Meré J, Saralegui U, et al. Non-invasive ambient intelligence in real life:dealing with noisy patterns to help older people[J]. Sensors(Basel), 2019, 19(14):3113. |
| [3] |
孙柳, 王莹, 梁嘉贵, 等. 老年人应对慢性病共病体验的Meta整合[J]. 中华护理杂志, 2022, 57(6):748-755.
DOI |
| Sun L, Wang Y, Liang JG, et al. Experience of the elderly coping with multiple chronic conditions:a qualitative Meta-synthesis[J]. Chin J Nurs, 2022, 57(6):748-755. | |
| [4] | Bloom DE, Chen SM, Kuhn M, et al. The economic burden of chronic diseases:estimates and projections for China,Japan and south Korea[J]. J Econ Ageing, 2017, 17:11-19. |
| [5] | The State Council of the People’s Republic of China. Development plan for the new generation of AI[EB/OL]. (2017-07-24)[2022-12-30]. http://www.scio.gov.cn/34473/34515/Document/1559231/1559231.htm. |
| [6] |
Stead WW. Clinical implications and challenges of artificial intelligence and deep learning[J]. JAMA, 2018, 320(11):1107-1108.
DOI PMID |
| [7] |
van Bussel MJP, Odekerken-Schröder GJ, Ou C, et al. Analyzing the determinants to accept a virtual assistant and use cases among cancer patients:a mixed methods study[J]. BMC Health Serv Res, 2022, 22(1):890.
DOI |
| [8] |
Amisha, Malik P, Pathania M, et al. Overview of artificial intelligence in medicine[J]. J Family Med Prim Care, 2019, 8(7):2328-2331.
DOI PMID |
| [9] |
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine[J]. Gastrointest Endosc, 2020, 92(4):807-812.
DOI PMID |
| [10] |
Yoo H, Kim KH, Singh R, et al. Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs[J]. JAMA Netw Open, 2020, 3(9):e2017135.
DOI URL |
| [11] |
Soh DCK, Ng EYK, Jahmunah V, et al. A computational intelligence tool for the detection of hypertension using empirical mode decomposition[J]. Comput Biol Med, 2020, 118:103630.
DOI URL |
| [12] |
Ong E, Wong MU, Huffman A, et al. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning[J]. Front Immunol, 2020, 11:1581.
DOI PMID |
| [13] |
Alamgir A, Mousa O, Shah Z. Artificial intelligence in predicting cardiac arrest:scoping review[J]. JMIR Med Inform, 2021, 9(12):e30798.
DOI URL |
| [14] |
Liu Y, Kohlberger T, Norouzi M, et al. Artificial intelligence-based breast cancer nodal metastasis detection:insights into the black box for pathologists[J]. Arch Pathol Lab Med, 2019, 143(7):859-868.
DOI |
| [15] |
Dagliati A, Marini S, Sacchi L, et al. Machine learning methods to predict diabetes complications[J]. J Diabetes Sci Technol, 2018, 12(2):295-302.
DOI PMID |
| [16] |
Liu Q, Zhang M, He YF, et al. Predicting the risk of incident type 2 diabetes mellitus in Chinese elderly using machine learning techniques[J]. J Pers Med, 2022, 12(6):905.
DOI URL |
| [17] |
Ye CY, Fu TY, Hao SY, et al. Prediction of incident hypertension within the next year:prospective study using statewide electronic health records and machine learning[J]. J Med Internet Res, 2018, 20(1):e22.
DOI URL |
| [18] | 张振. 基于机器学习的心血管疾病风险预测关键技术研究[D]. 成都: 电子科技大学, 2021. |
| Zhang Z. Research on key techniques of cardiovascular diseases risk prediction based on machine learning[D]. Chengdu: University of Electronic Science and Technology of China, 2021. | |
| [19] |
孙宏玉, 孙玉梅, 孙敬怡, 等. 基于智能健康监测系统的社区居民健康状况及影响因素分析[J]. 中华护理杂志, 2020, 55(12):1836-1843.
DOI |
|
Sun HY, Sun YM, Sun JY, et al. Analysis of health status and its influencing factors of community residents based on the intelligent health monitoring system[J]. Chin J Nurs, 2020, 55(12):1836-1843.
DOI |
|
| [20] | Chatterjee A, Gerdes MW, Martinez SG. Identification of risk factors associated with obesity and overweight:a machine learning overview[J]. Sensors(Basel), 2020, 20(9):2734. |
| [21] |
Caballero-Ruiz E, García-Sáez G, Rigla M, et al. A web-based clinical decision support system for gestational diabetes:automatic diet prescription and detection of insulin needs[J]. Int J Med Inform, 2017, 102:35-49.
DOI PMID |
| [22] |
Faruqui SHA, Du Y, Meka R, et al. Development of a deep learning model for dynamic forecasting of blood glucose level for type 2 diabetes mellitus:secondary analysis of a randomized controlled trial[J]. JMIR Mhealth Uhealth, 2019, 7(11):e14452.
DOI URL |
| [23] |
Goldenhersch E, Thrul J, Ungaretti J, et al. Virtual reality smartphone-based intervention for smoking cessation:pilot randomized controlled trial on initial clinical efficacy and adherence[J]. J Med Internet Res, 2020, 22(7):e17571.
DOI URL |
| [24] | 何金超, 罗芳, 袁知才, 等. 协同过滤和粒子群算法在饮食推荐中的应用[J]. 计算机应用与软件, 2019, 36(8):36-40,59. |
|
He JC, Luo F, Yuan ZC, et al. Application of collaborative filtering and particle swarm optimization in dietary recommendation[J]. Comput Appl Softw, 2019, 36(8):36-40,59.
DOI URL |
|
| [25] |
Maeta K, Nishiyama Y, Fujibayashi K, et al. Prediction of glucose metabolism disorder risk using a machine learning algorithm:pilot study[J]. JMIR Diabetes, 2018, 3(4):e10212.
DOI URL |
| [26] | Vanegas E, Igual R, Plaza I. Sensing systems for respiration monitoring:a technical systematic review[J]. Sensors(Basel), 2020, 20(18):5446. |
| [27] | Mukherjee D, Dhar K, Schwenker F, et al. Ensemble of deep learning models for sleep apnea detection:an experimental study[J]. Sensors(Basel), 2021, 21(16):5425. |
| [28] | 吕煜焱, 丁思霄, 赵逸凡, 等. 人工智能化的远程心电监测在心血管疾病中的应用[J]. 中国心血管杂志, 2020, 25(3):270-273. |
| Lü YY, Ding SX, Zhao YF, et al. Application of remote ECG monitoring with artificial intelligence in cardiovascular diseases[J]. Chin J Cardiovasc Med, 2020, 25(3):270-273. | |
| [29] |
Li J, Huang J, Zheng LB, et al. Application of artificial intelligence in diabetes education and management:present status and promising prospect[J]. Front Public Health, 2020, 8:173.
DOI URL |
| [30] | 唐晓波, 郑杜, 谭明亮. 慢性病健康教育知识服务系统模型构建研究[J]. 情报科学, 2019, 37(1):134-140. |
| Tang XB, Zheng D, Tan ML. Model construction of health education knowledge service system for chronic diseases[J]. Inf Sci, 2019, 37(1):134-140. | |
| [31] | 毛佳伊, 谢莉玲. 老年慢性病病人口服药智能化管理研究进展[J]. 护理研究, 2021, 35(15):2706-2709. |
| Mao JY, Xie LL. Research progress on intelligent management of oral medications for elderly patients with chronic diseases[J]. Chin Nurs Res, 2021, 35(15):2706-2709. | |
| [32] |
Midão L, Giardini A, Menditto E, et al. Polypharmacy prevalence among older adults based on the survey of health,ageing and retirement in Europe[J]. Arch Gerontol Geriatr, 2018, 78:213-220.
DOI URL |
| [33] |
Turjamaa R, Kapanen S, Kangasniemi M. How smart medication systems are used to support older people’s drug regimens:a systematic literature review[J]. Geriatr Nurs, 2020, 41(6):677-684.
DOI PMID |
| [34] |
Labovitz DL, Shafner L, Gil MR, et al. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy[J]. Stroke, 2017, 48(5):1416-1419.
DOI PMID |
| [35] |
Persell SD, Peprah YA, Lipiszko D, et al. Effect of home blood pressure monitoring via a smartphone hypertension coaching application or tracking application on adults with uncontrolled hypertension:a randomized clinical trial[J]. JAMA Netw Open, 2020, 3(3):e200255.
DOI URL |
| [36] |
彭一航, 谢莉玲, 梁燕, 等. 智能药物管理系统在养老机构老年人中的应用[J]. 中华护理杂志, 2021, 56(5):680-686.
DOI |
|
Peng YH, Xie LL, Liang Y, et al. Efficacy of intelligent medication management system in elderly people in care institutions[J]. Chin J Nurs, 2021, 56(5):680-686.
DOI |
|
| [37] |
Ranzani R, Lambercy O, Metzger JC, et al. Neurocognitive robot-assisted rehabilitation of hand function:a randomized control trial on motor recovery in subacute stroke[J]. J Neuroeng Rehabil, 2020, 17(1):115.
DOI |
| [38] | Dhiman A, Solanki D, Bhasin A, et al. An intelligent,adaptive,performance-sensitive,and virtual reality-based gaming platform for the upper limb[J]. Comput Anim Virtual Worlds, 2018, 29(2): e1800. |
| [39] | de Luca R, Maggio MG, Maresca G, et al. Improving cognitive function after traumatic brain injury:a clinical trial on the potential use of the semi-immersive virtual reality[J]. Behav Neurol, 2019, 2019:9268179. |
| [40] |
Oh YJ, Zhang JW, Fang ML, et al. A systematic review of artificial intelligence chatbots for promoting physical activity,healthy diet,and weight loss[J]. Int J Behav Nutr Phys Act, 2021, 18(1):160.
DOI |
| [41] |
Lee SH, Lee HJ, Chang WH, et al. Gait performance and foot pressure distribution during wearable robot-assisted gait in elderly adults[J]. J Neuroeng Rehabil, 2017, 14(1):123.
DOI URL |
| [42] |
Dinesen B, Hansen HK, Grønborg GB, et al. Use of a social robot(LOVOT) for persons with dementia:exploratory study[J]. JMIR Rehabil Assist Technol, 2022, 9(3):e36505.
DOI URL |
| [43] | 吴雪梅. 人工智能系统在乳腺癌内分泌治疗依从性及副反应管理中的应用研究[D]. 昆明: 昆明医科大学, 2022. |
| Wu XM. Research on artificial intelligence system in endocrine therapy compliance and the side reactions management of breast cancer[D]. Kunming: Kunming Medical University, 2022. | |
| [44] |
Fulmer R, Joerin A, Gentile B, et al. Using psychological artificial intelligence(tess) to relieve symptoms of depression and anxiety:randomized controlled trial[J]. JMIR Ment Health, 2018, 5(4):e64.
DOI URL |
| [1] | 中华护理学会静脉输液治疗专业委员会, 北京护理学会, (执笔:李佳, 李旭英, 覃惠英, 陈利芬, 吴珍明, 胡泽吟, 罗梦娜, 范育英, 吴嘉慧, 林宇萍, 王蕾, 孙文彦, 冯毕龙, 高伟, 李亚南, 侯罗娅, 李春燕. 中心静脉通路装置拔除护理专家共识[J]. 中华护理杂志, 2026, 61(9): 1157-1162. |
| [2] | 周霞, 马艳艳, 师正坤, 于海霞, 周艳, 胡红玲, 李莜, 陈思思, 张京慧. 隧道式经股静脉PICC置入术在困难静脉通路婴幼儿中的应用研究[J]. 中华护理杂志, 2026, 61(9): 1163-1170. |
| [3] | 黄萍, 李黎, 郭骊莉, 徐莹, 叶赟, 江淑芳. 与无针输液接头相关的导管相关性血流感染风险评估工具的构建与初步应用[J]. 中华护理杂志, 2026, 61(9): 1171-1179. |
| [4] | 李佳, 张玉玲, 邢乐, 张利峰, 牛秀峰, 许莉. 早产儿经上肢置入PICC尖端继发性异位风险预测模型的建立与验证[J]. 中华护理杂志, 2026, 61(9): 1180-1186. |
| [5] | 何娟, 赵蕾蕾, 叶冠军, 余艳芬, 傅晓君, 周琴. 1例双侧无名静脉及上腔静脉人工血管置换术后患者经上肢置入PICC的护理[J]. 中华护理杂志, 2026, 61(9): 1187-1191. |
| [6] | 梁江淑渊, 曾妃, 何鹏, 谢梦珊, 江悦, 蒋宗恒, 王建茗. 静脉-静脉体外膜肺氧合患者早期活动风险等级评估系统的构建及应用研究[J]. 中华护理杂志, 2026, 61(9): 1192-1199. |
| [7] | 陈晨, 顾肖, 郭凡, 王敏, 倪兴梅, 黄琴, 程念开. 3种衰弱评估工具对急诊老年创伤患者不良结局的预测价值[J]. 中华护理杂志, 2026, 61(9): 1200-1207. |
| [8] | 郑煜琳, 刘晋宁, 张静, 郭会敏, 谷艳梅, 张莉莉. 酒精性肝病患者肝移植术后再饮酒现状及其影响因素分析[J]. 中华护理杂志, 2026, 61(9): 1208-1214. |
| [9] | 叶俊霜, 沈丽佳, 孟霞靓, 张玉姣, 蔡根莲, 邵碧云, 周亚辉, 张柳倩, 应金萍. 维持性血液透析患者对口腔衰弱认知和体验的质性研究[J]. 中华护理杂志, 2026, 61(9): 1215-1220. |
| [10] | 王芸姣, 杨玉金, 郑春艳, 彭菲津, 王淑贞, 蒋喜露, 周为民, 王薇, 张娜. 糖尿病下肢动脉硬化闭塞症患者介入术后康复护理方案的构建及应用研究[J]. 中华护理杂志, 2026, 61(9): 1221-1229. |
| [11] | 杨明霞, 安冉, 臧金凤, 李春梅. 老年慢性病共病患者及照护者老老照护能力与自我忽视的相关性分析[J]. 中华护理杂志, 2026, 61(9): 1230-1236. |
| [12] | 梁和静, 许鹊, 史云霞, 祖金美, 黄文静, 杨春旭, 郭明华, 王磊. 颈动脉内膜剥脱术围手术期护理质量评价指标的构建[J]. 中华护理杂志, 2026, 61(9): 1237-1244. |
| [13] | 韩媛媛, 石玉竹, 尚文涵, 么莉. 870所三级甲等医院老年住院患者跌倒伤害发生现状与影响因素分析[J]. 中华护理杂志, 2026, 61(9): 1245-1252. |
| [14] | 夏云芳, 王洪, 崔岱, 付真真, 朱敏. 1例弥漫性毒性甲状腺肿合并超敏反应综合征青少年患者的护理[J]. 中华护理杂志, 2026, 61(9): 1253-1256. |
| [15] | 周益飞, 王海苹, 胡益环, 应莉, 罗松娜. 1例氢氟酸中毒并发呼吸心搏骤停患者的急救护理[J]. 中华护理杂志, 2026, 61(9): 1257-1260. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||