In order to meet the needs of the human-computer interaction experience of health testing products and improve the decision-making efficiency of intelligent decision support systems, we visualized the design of health testing products. We summarized the design methods for the human-computer interaction experience of health testing products, analyzed health testing data visualization requirements in terms of thematic databases, data visualization diagrams, thematic dashboards and knowledge management systems, and introduced the general process of monitoring information visualization. The visual health testing product information display interface is designed to visualize the testing data in three aspects: information architecture, interaction mode and visual language presentation. The visual intelligent decision support system and the visual interface design are combined for the functional design of the visual intelligent decision support system. The experimental part of the study investigates the effectiveness of the visualized health testing product of the intelligent decision support system, using the questionnaire method and health data measurement method to collect results on the interactivity, convenience, health decision accuracy and product satisfaction of the health monitoring product, with the data presented as a percentage system. The experimental results show that the interactivity, convenience and health decision accuracy of the intelligent decision support visual health monitoring product are higher than those of traditional health monitoring products, with interactivity evaluation results above 85% and high satisfaction with product use, indicating that the product can provide new and innovative design ideas in home healthcare.
Citation: Yinhua Su. Visualization design of health detection products based on human-computer interaction experience in intelligent decision support systems[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 16725-16743. doi: 10.3934/mbe.2023745
In order to meet the needs of the human-computer interaction experience of health testing products and improve the decision-making efficiency of intelligent decision support systems, we visualized the design of health testing products. We summarized the design methods for the human-computer interaction experience of health testing products, analyzed health testing data visualization requirements in terms of thematic databases, data visualization diagrams, thematic dashboards and knowledge management systems, and introduced the general process of monitoring information visualization. The visual health testing product information display interface is designed to visualize the testing data in three aspects: information architecture, interaction mode and visual language presentation. The visual intelligent decision support system and the visual interface design are combined for the functional design of the visual intelligent decision support system. The experimental part of the study investigates the effectiveness of the visualized health testing product of the intelligent decision support system, using the questionnaire method and health data measurement method to collect results on the interactivity, convenience, health decision accuracy and product satisfaction of the health monitoring product, with the data presented as a percentage system. The experimental results show that the interactivity, convenience and health decision accuracy of the intelligent decision support visual health monitoring product are higher than those of traditional health monitoring products, with interactivity evaluation results above 85% and high satisfaction with product use, indicating that the product can provide new and innovative design ideas in home healthcare.
[1] | J. Kim, Wearable biosensors for healthcare monitoring, Nat. Biotechnol., 37 (2019), 389–406. https://doi.org/10.1038/s41587-019-0045-y doi: 10.1038/s41587-019-0045-y |
[2] | J. Li, Health monitoring through wearable technologies for older adults: Smart wearables acceptance model, Appl. Ergon., 75 (2019), 162–169. https://doi.org/10.1016/j.apergo.2018.10.006 doi: 10.1016/j.apergo.2018.10.006 |
[3] | W. Xu, Toward human-centered AI: a perspective from human-computer interaction, Interactions, 26 (2019), 42–46. https://doi.org/10.1145/3328485 doi: 10.1145/3328485 |
[4] | A. Vellido, The importance of interpretability and visualization in machine learning for applications in medicine and health care, Neural Comput. Appl., 32 (2020), 18069–18083. https://doi.org/10.1007/s00521-019-04051-w doi: 10.1007/s00521-019-04051-w |
[5] | R. Blazek, L. Hrosova, J. Collier, Internet of medical things-based clinical decision support systems, smart healthcare wearable devices, and machine learning algorithms in COVID-19 prevention, screening, detection, diagnosis, and treatment, Am. J. Med. Res., 9 (2022), 65–80. https://doi.org/10.22381/ajmr9120225 doi: 10.22381/ajmr9120225 |
[6] | R. Nimri, Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes, Nat. Med., 26 (2020), 1380–1384. https://doi.org/10.1038/s41591-020-1045-7 doi: 10.1038/s41591-020-1045-7 |
[7] | P. Zuiev, R. Zhyvotovskyi, O. Zvieriev, S. Hatsenko, V. Kuprii, O. Nakonechnyi, et al., Development of complex methodology of processing heterogeneous data in intelligent decision support systems, East. Eur. J. Enterp. Technol., 106 (2020), 14–23. https://doi.org/10.15587/1729-4061.2020.208554 doi: 10.15587/1729-4061.2020.208554 |
[8] | G. Mahadevaiah, Artificial intelligence‐based clinical decision support in modern medical physics: selection, acceptance, commissioning, and quality assurance, Med. Phys., 47 (2020), e228–e235. https://doi.org/10.1002/mp.13562 doi: 10.1002/mp.13562 |
[9] | J. Zhang, Architecture and design of a wearable robotic system for body posture monitoring, correction, and rehabilitation assist, Int. J. Soc. Rob., 11 (2019), 423–436. https://doi.org/10.1007/s12369-019-00512-3 doi: 10.1007/s12369-019-00512-3 |
[10] | X. Su, Cloud–edge collaboration-based bi-level optimal scheduling for intelligent healthcare systems, Future Gener. Comput. Syst., 141 (2023), 28–39. https://doi.org/10.1016/j.future.2022.11.005 doi: 10.1016/j.future.2022.11.005 |
[11] | A. Rapp, In search for design elements: a new perspective for employing ethnography in human-computer interaction design research, Int. J. Human Comput. Interact., 37 (2021), 783–802. https://doi.org/10.1080/10447318.2020.1843296 doi: 10.1080/10447318.2020.1843296 |
[12] | J. Zhang, Thermal perception for information transmission: Theoretical analysis, device design, and experimental verification, IEEE Trans. Haptics, 15 (2022), 679–692. https://doi.org/10.1109/TOH.2022.3208937 doi: 10.1109/TOH.2022.3208937 |
[13] | Z. Guney, Considerations for human-computer interaction: User interface design variables and visual learning in IDT, Cypriot J. Edu. Sci., 14 (2019), 731–741. https://doi.org/10.18844/cjes.v14i4.4481 doi: 10.18844/cjes.v14i4.4481 |
[14] | A. Joseph, R. Murugesh, Potential eye tracking metrics and indicators to measure cognitive load in human-computer interaction research, J. Sci. Res., 64 (2020), 168–175. https://doi.org/10.37398/JSR.2020.640137 doi: 10.37398/JSR.2020.640137 |
[15] | A. Sodhro, An adaptive QoS computation for medical data processing in intelligent healthcare applications, Neural Comput. Appl., 32 (2020), 723–734. https://doi.org/10.1007/s00521-018-3931-1 doi: 10.1007/s00521-018-3931-1 |
[16] | Y. Yun, D. Ma, M. Yang, Human–computer interaction-based decision support system with applications in data mining, Future Gener. Comput. Syst., 114 (2021), 285–289. https://doi.org/10.1016/j.future.2020.07.048 doi: 10.1016/j.future.2020.07.048 |
[17] | A. Chinnaswamy, Big data visualisation, geographic information systems and decision making in healthcare management, Manage. Decis., 57 (2019), 1937–1959. https://doi.org/10.1108/MD-07-2018-0835 doi: 10.1108/MD-07-2018-0835 |
[18] | S. Sung, P. Lee, C. Hsieh, W. Zheng, Medication use and the risk of newly diagnosed diabetes in patients with epilepsy: A data mining application on a healthcare database, J. Organ. End User Comput., 32 (2020), 93–108. https://doi.org/10.4018/JOEUC.2020040105 doi: 10.4018/JOEUC.2020040105 |
[19] | H. Manohar, Design of distributed database system based on improved DES algorithm, Distrib. Process. Syst., 3 (2022), 19–27. https://doi.org/10.38007/DPS.2022.030403 doi: 10.38007/DPS.2022.030403 |
[20] | R. Zhao, Deep learning and its applications to machine health monitoring, Mech. Syst. Signal Process., 115 (2019), 213–237. https://doi.org/10.1016/j.ymssp.2018.05.050 doi: 10.1016/j.ymssp.2018.05.050 |
[21] | Y. Bao, Computer vision and deep learning–based data anomaly detection method for structural health monitoring, Struct. Health Monit., 18 (2019), 401–421. https://doi.org/10.1177/1475921718757405 doi: 10.1177/1475921718757405 |
[22] | C. J. Turner, Intelligent decision support for maintenance: an overview and future trends, Int. J. Comput. Integr. Manuf., 32 (2019), 936–959. https://doi.org/10.1080/0951192X.2019.1667033 doi: 10.1080/0951192X.2019.1667033 |
[23] | A. Polyakova, Managerial decision support algorithm based on network analysis and big data, Int. J. Civil Eng. Technol., 10 (2019), 291–300. |