Abstract
Chronic diseases have the characteristics of high morbidity, low awareness, and high disability and fatalities, which have a huge impact on human health. How to optimize chronic disease management through existing technologies is a worthy research direction. This article takes chronic disease management as the research object, and designs an artificial intelligence chronic disease management system based on medical resource perception by combining technologies such as Artificial Intelligence (AI), user portrait, and Knowledge Graph (KG). Supported by multi-dimensional medical big data, through multi-party linkage, the functions of patient-oriented risk assessment, hierarchical diagnosis and treatment, and diagnosis and treatment decision assistance for medical workers and follow-up planning assistance are realized. The project achieved 60,243 patient-time management of chronic disease patients through cooperation with a tertiary grade A hospital in Nanjing, and the drug compliance of chronic disease patients was 94.5%. Practice results demonstrate that the system can promote the efficient and orderly operation of the chronic disease management ecology.
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References
World Health Organization. World Health Statistics 2018: Monitoring health for the SDGs. World Health Organization, Geneva (2018)
Rutledge, G.E., Kimberly, L., Caitlin, M., et al.: Coordinated approaches to strengthen state and local public health actions to prevent obesity, diabetes, and heart disease and stroke. Prev. Chronic Dis. 15, 170493 (2018)
Progress in disease prevention and control in China. Capital Public Health 9(03), 97–101 (2015)
Notice of the General Office of the State Council on Printing and Distributing China’s Medium and Long-term Plan for the Prevention and Treatment of Chronic Diseases (2017–2025). The State Council Bulletin of the People’s Republic of China (07), 17–24 (2017)
World Health Organization. Noncommunicable diseases country profiles 2018 [EB/OL]. https://www.who.int/nmh/publications/ncd-profiles-2018/en/
Xiu, L., He, H., Chen, E., et al.: Research on the cost difference and compensation of basic public health services in the counties of Zhejiang Province. China Health Econ. 31(9), 53–55 (2012)
Xia, T., Zhao, Z., Hou, W., et al.: Research on the cost of community health education services in Shenzhen. Chin. Gen. Pract. 13(8), 1303–1306 (2015)
Zhuo, Z., et al.: Study on the cost-effectiveness evaluation of community health management for people with dyslipidemia in Shenzhen. Chin. J. Health Manag. 12(04), 313–318 (2018)
Chen, K., Zhang, A., Ye, J.: Research on the equilibrium of my country’s medical and health resources allocation based on the degree of agglomeration. Chin. Med. Manag. Sci. 10(05), 5–10 (2020)
Yang, L., Gao, Y., Wu, S., Li, H.: Research on influencing factors of community chronic disease management based on service quality gap model. Soft Sci. Health 34(07), 60–65 (2020)
Zhang, M., Xiao, Y., Yuan, J., Li, X.: Study on the status quo of community health management of chronic diseases in my country. J. Chengdu Med. Coll. 14(05), 650–653+657 (2019)
Istepanian, R., Laxminarayan, S., Pattichis, C.S.: M-Health: Emergin Mobile Health Systems. Springer, London (2010)
MacKinnon, G., Brittain, E.: Mobile health technologies in cardiopulmonary disease. Chest 157(3), 654–664 (2020). https://doi.org/10.1016/j.chest.2019.10.015
Wang, H.: Establishment and evaluation of internet-based community hypertension self-management intervention model. Jinan University (2017)
Chen, J.: Application research of remote ECG monitoring in out-of-hospital management of patients with chronic heart failure. Kunming Medical University (2017)
Wang, D., Ding, M.: Impact of network-based continuous follow-up nursing on postoperative rehabilitation and quality of life of patients with cardiac pacemaker implantation. Gen. Nurs. 16(04), 499–501 (2018)
Luo, H., Zhang, F., Qi, R., Lin, Y.: Construction of “Internet +” health education program for cancer patients. Chin. J. Nurs. 52(12), 1482–1485 (2017)
Huang, X., et al.: Discussion on the implementation path of precision diagnosis and treatment based on big data of health care. Chin. J. Hosp. Manag. 33(05), 369–372 (2017)
Guo, C., Chen, J.: Big data analytics in healthcare: data-driven methods for typical treatment pattern mining. J. Syst. Sci. Syst. Eng. 28(6), 694–714 (2019). https://doi.org/10.1007/s11518-019-5437-5
Bahri, S., Zoghlami, N., Abed, M., Tavares, J.M.R.S.: Big data for healthcare: a survey. IEEE Access 7, 7397–7408 (2019). https://doi.org/10.1109/ACCESS.2018.2889180
Xu, T., Yu, G.: Application scenarios and value analysis of big data sharing in health care. China Digit. Med. 15(07), 1–3 (2020)
Wu, Q., Miao, R., Song, Y., Cheng, Y., Jiang, Z.: Research on medical resource allocation decision-making for hierarchical diagnosis and treatment. Ind. Eng. Manag. 23(03), 150–156 (2018)
Sfar, A.R., Challal, Y., Moyal, P., et al.: A game theoretic approach for privacy preserving model in IoT-based transportation. IEEE Trans. Intell. Transp. Syst. 99, 1–10 (2019)
Wen, T., Zhang, Z., Qiu, M., Qingfeng, W., Li, C.: A multi-objective optimization method for emergency medical resources allocation. J. Med. Imaging Health Inform. 7(2), 393–399 (2017). https://doi.org/10.1166/jmihi.2017.2027
Zhou, Y.: Thinking about the allocation of medical resources under the background of hierarchical diagnosis and treatment. New 14(04), 27–36 (2020)
Acknowledgment
This paper was supported by the National Natural Science Foundation of China (61802208 and 61772286), Project funded by China Postdoctoral Science Foundation (2019M651923 and 2020M671552), Natural Science Foundation of Jiangsu Province of China (BK20191381), Primary Research & Development Plan of Jiangsu Province Grant (BE2019742), the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software (No. 2020DS301).
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Ma, Y., Chen, G., Yan, W., Xu, B., Qi, J. (2021). Artificial Intelligence Chronic Disease Management System Based on Medical Resource Perception. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_6
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