Progress on the application of artificial intelligence in chronic disease health management

doi:10.3761/j.issn.0254-1769.2023.09.006

Abstract

Abstract:

Chronic disease is one of the public health problems that threatens the health of patients，with long-term and incurable characteristics. The development of artificial intelligence provides the possibility for achieving efficient health management of chronic diseases. This paper introduces the overview of artificial intelligence and its application in the chronic disease health management，including disease-risk prediction，lifestyle optimization，disease monitoring，drug management，rehabilitation training and daily care. Meanwhile，the existing challenges and expectations were summarized，in order to provide references for the application of artificial intelligence in chronic disease health management in China.

Key words: Artificial Intelligence, Chronic Disease, Health Management, Nursing Care, Review

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。

关键词: 人工智能, 慢性病, 健康管理, 护理, 综述

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.

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.

References 44

[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