Research article Special Issues

A knowledge-driven decision support system for remote medical management


  • Received: 27 September 2022 Revised: 02 November 2022 Accepted: 07 November 2022 Published: 28 November 2022
  • Residential medical digital technology is an emerging discipline combining computer network technology and medical research. Based on the idea of knowledge discovery, this study was designed to construct a decision support system for remote medical management, analyze the need for utilization rate calculations and obtain relevant modeling elements for system design. Specifically, the model constructs a design method for a decision support system for the healthcare management of elderly residents through the use of a utilization rate modeling method based on digital information extraction. In the simulation process, the utilization rate modeling and system design intent analysis are combined to obtain the relevant functions and morphological characteristics that are essential to the system. Using regular usage slices, a higher-precision non-uniform rational B-spline (NURBS) usage rate can be fitted and a surface model with better continuity can be constructed. The experimental results show that the deviation of the NURBS usage rate generated by the boundary division from the original data model can reach test accuracies of 83, 87 and 89%, respectively. It is shown that the method can effectively reduce the modeling error caused by the irregular feature model in the process of modeling the utilization rate of digital information, and that it can ensure the accuracy of the model.

    Citation: Yuqing Lu. A knowledge-driven decision support system for remote medical management[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2732-2749. doi: 10.3934/mbe.2023128

    Related Papers:

  • Residential medical digital technology is an emerging discipline combining computer network technology and medical research. Based on the idea of knowledge discovery, this study was designed to construct a decision support system for remote medical management, analyze the need for utilization rate calculations and obtain relevant modeling elements for system design. Specifically, the model constructs a design method for a decision support system for the healthcare management of elderly residents through the use of a utilization rate modeling method based on digital information extraction. In the simulation process, the utilization rate modeling and system design intent analysis are combined to obtain the relevant functions and morphological characteristics that are essential to the system. Using regular usage slices, a higher-precision non-uniform rational B-spline (NURBS) usage rate can be fitted and a surface model with better continuity can be constructed. The experimental results show that the deviation of the NURBS usage rate generated by the boundary division from the original data model can reach test accuracies of 83, 87 and 89%, respectively. It is shown that the method can effectively reduce the modeling error caused by the irregular feature model in the process of modeling the utilization rate of digital information, and that it can ensure the accuracy of the model.



    加载中


    [1] Z. Guo, K. Yu, A. K. Bashir, D. Zhang, Y. D. Al-Otaibi, M. Guizani, Deep information fusion-driven POI scheduling for mobile social networks, IEEE Network, 36 (2022), 210-216. https://doi.org/10.1109/MNET.102.2100394 doi: 10.1109/MNET.102.2100394
    [2] Y. Li, H. Ma, L. Wang, S. Mao, G. Wang, Optimized content caching and user association for edge computing in densely deployed heterogeneous networks, IEEE Trans. Mob. Comput., 21 (2020), 2130-2142. https://doi.org/10.1109/TMC.2020.3033563 doi: 10.1109/TMC.2020.3033563
    [3] L. Zhao, Z. Bi, A. Hawbani, K. Yu, Y. Zhang, M. Guizani, ELITE: An intelligent digital twin-based hierarchical routing scheme for softwarized vehicular nnetworks, IEEE Trans. Mob. Comput., 2022. https://doi.org/10.1109/TMC.2022.3179254 doi: 10.1109/TMC.2022.3179254
    [4] Z. Guo, K. Yu, Z. Lv, K. K. R. Choo, P. Shi, J. J. P. C. Rodrigues, Deep federated learning enhanced secure POI microservices for cyber-physical systems, IEEE Wireless Commun., 29 (2022), 22-29. https://doi.org/10.1109/MWC.002.2100272 doi: 10.1109/MWC.002.2100272
    [5] Q. Zhang, K. Yu, Z. Guo, S. Garg, J. J. P. C. Rodrigues, M. M. Hassan, et al., Graph neural networks-driven traffic forecasting for connected internet of vehicles, IEEE Trans. Network Sci. Eng., 9 (2022), 3015-3027. https://doi.org/10.1109/TNSE.2021.3126830 doi: 10.1109/TNSE.2021.3126830
    [6] S. Xia, Z. Yao, Y. Li, S. Mao, Online distributed offloading and computing resource management with energy harvesting for heterogeneous MEC-enabled IoT, IEEE Trans. Wireless Commun., 20 (2021), 6743-6757. https://doi.org/10.1109/TWC.2021.3076201 doi: 10.1109/TWC.2021.3076201
    [7] Z. Zhou, X. Dong, Z. Li, K. Yu, C. Ding, Y. Yang, Spatio-temporal feature encoding for traffic accident detection in VANET environment, IEEE Trans. Intell. Transp. Syst., 23 (2022), 19772-19781. https://doo.org/10.1109/TITS.2022.3147826
    [8] B. Zhu, K. Chi, J. Liu, K. Yu, S. Mumtaz, Efficient offloading for minimizing task computation delay of NOMA-based multi-access edge computing, IEEE Trans. Commun., 70 (2022), 3186-3203. https://doi.org/10.1109/TCOMM.2022.3162263 doi: 10.1109/TCOMM.2022.3162263
    [9] J. Wei, Q. Zhu, Q. Li, L. Nie, Z. Shen, K. K. R. Choo, et al., A redactable blockchain framework for secure federated learning in industrial internet-of-things, IEEE Internet Things J., 9 (2022), 17901-17911. https://doi.org/10.1109/JIOT.2022.3162499 doi: 10.1109/JIOT.2022.3162499
    [10] D. M. Walker, J. L. Hefner, N. Fareed, T. R. Huerta, A. S. McAlearney, Exploring the digital divide: age and race disparities in use of an inpatient portal, Telemed. e-Health, 26 (22020), 603-613. https://doi.org/10.1089/tmj.2019.0065
    [11] V. Botrić, L. Božić, The digital divide and E-government in European economies, Econ. Res.-Ekon. Istraž., 34 (2021), 2935-2955. https://doi.org/10.1080/1331677X.2020.1863828 doi: 10.1080/1331677X.2020.1863828
    [12] J. Choudrie, S. Pheeraphuttranghkoon, S. Davari, The digital divide and older adult population adoption, use and diffusion of mobile phones: a quantitative study, Inf. Syst. Front., 22 (2020), 673-695. https://doi.org/10.1007/s10796-018-9875-2 doi: 10.1007/s10796-018-9875-2
    [13] Z. Guo, Y. Shen, S. Wan, W. Shang, K. Yu, Hybrid intelligence-driven medical image recognition for remote patient diagnosis in internet of medical things, IEEE J. Biomed. Health. Inf., 2021. https://doi.org/10.1109/JBHI.2021.3139541. doi: 10.1109/JBHI.2021.3139541
    [14] K. Yu, L. Tan, S. Mumtaz, S. AI-Rubaye, A. AI-Dulaimi, A. K. Bashir, et al., Securing critical infrastructures: deep-learning-based threat detection in IIoT, IEEE Commun. Mag., 59 (2021), 76-82. https://doi.org/10.1109/MCOM.101.2001126 doi: 10.1109/MCOM.101.2001126
    [15] V. Balakrishnan, N. L. M. Shuib, Drivers and inhibitors for digital payment adoption using the Cashless Society Readiness-Adoption model in Malaysia, Technol. Soc., 65 (2021), 101554. https://doi.org/10.1016/j.techsoc.2021.101554 doi: 10.1016/j.techsoc.2021.101554
    [16] M. A. Kaium, Y. Bao, M. Z. Alam, M. R. Hoque, Understanding continuance usage intention of mHealth in a developing country: an empirical investigation, Int. J. Pharm. Healthcare Mark., 13 (2020), 73-82.
    [17] S. Iftikhar, A. Saqib, M. R. Sarwar, M. Sarfraz, M. Arafat, Q. Shoaib, Capacity and willingness to use information technology for managing chronic diseases among patients: a cross-sectional study in Lahore, Pakistan, PLoS One, 14 (2019), e0209654. https://doi.org/10.1371/journal.pone.0209654 doi: 10.1371/journal.pone.0209654
    [18] P. K. Beh, Y. Ganesan, M. Iranmanesh, B. Foroughi, Using smartwatches for fitness and health monitoring: the UTAUT2 combined with threat appraisal as moderators, Behav. Inf. Technol., 40 (2019), 282-299. https://doi.org/10.1080/0144929X.2019.1685597 doi: 10.1080/0144929X.2019.1685597
    [19] F. O. Oderanti, F. Li, M. Cubric, X. Shi, Business models for sustainable commercialisation of digital healthcare (eHealth) innovations for an increasingly ageing population, Technol. Forecasting Social Change, 171 (2021), 120969. https://doi.org/10.1016/j.techfore.2021.120969 doi: 10.1016/j.techfore.2021.120969
    [20] O. H. Salman, Z. Taha, M. Q. Alsabah, Y. S. Hussein, A. S. Mohammed, M. Aal-Nouman, A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work, Comput. Methods Programs Biomed., 209 (2021), 106357. https://doi.org/10.1016/j.cmpb.2021.106357 doi: 10.1016/j.cmpb.2021.106357
    [21] X. Zhang, Y. Wang, Research on intelligent medical big data system based on Hadoop and blockchain, EURASIP J. Wireless Commun. Networking, 2021 (2021), 16-21. https://doi.org/10.1186/s13638-020-01858-3 doi: 10.1186/s13638-020-01858-3
    [22] A. Ahmad, T. Rasul, A. Yousaf, U. Zaman, Understanding factors influencing elderly diabetic patients' continuance intention to use digital health wearables: extending the Technology Acceptance Model (TAM), J. Open Innov. Technol. Mark. Complex., 6 (2020), 81. https://doi.org/10.3390/joitmc6030081 doi: 10.3390/joitmc6030081
    [23] Q. Ma, A. H. S. Chan, P. L. Teh, Insights into older adults' technology acceptance through meta-analysis, Int. J. Hum.-Comput. Interact., 37 (2021), 1049-1062. https://doi.org/10.1080/10447318.2020.1865005 doi: 10.1080/10447318.2020.1865005
    [24] P. Yu, S. Qian, Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care, PLoS One, 13 (2018), e0190749. https://doi.org/10.1371/journal.pone.0190749 doi: 10.1371/journal.pone.0190749
    [25] W. Wu, D. Zhu, W. Liu, C. H. Wu, Empirical research on smart city construction and public health under information and communications technology, Socio-Econ. Plann. Sci., 80 (2020), 100994. https://doi.org/10.1016/j.seps.2020.100994 doi: 10.1016/j.seps.2020.100994
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1122) PDF downloads(63) Cited by(0)

Article outline

Figures and Tables

Figures(8)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog