Health literacy refers to the ability of individuals to obtain and understand health information and use it to maintain and promote their own health. This paper manages to make predictions toward its development degree in society with use of a big data-driven statistical learning method. Actually, such results can be analyzed by discovering latent rules from massive public textual contents. As a result, this paper proposes a deep information fusion-based smart prediction approach for health literacy. Specifically, the latent Dirichlet allocation (LDA) and convolutional neural network (CNN) structures are utilized as the basic backbone to understand semantic features of textual contents. The feature learning results of LDA and CNN can be then mapped into prediction results via following multi-dimension computing structures. After constructing the CNN model, we can input health information into the model for feature extraction. The CNN model can automatically learn valuable features from raw health information through multi-layer convolution and pooling operations. These characteristics may include lifestyle habits, physiological indicators, biochemical indicators, etc., reflecting the patient's health status and disease risk. After extracting features, we can train the CNN model through a training set and evaluate the performance of the model using a test set. The goal of this step is to optimize the parameters of the model so that it can accurately predict health information. We can use common evaluation indicators such as accuracy, precision, recall, etc. to evaluate the performance of the model. At last, some simulation experiments are conducted on real-world data collected from famous international universities. The case study analyzes health literacy difference between China of developed countries. Some prediction results can be obtained from the case study. The proposed approach can be proved effective from the discussion of prediction results.
Citation: Xiaoyan Zhao, Sanqing Ding. A multi-dimension information fusion-based intelligent prediction approach for health literacy[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 18104-18122. doi: 10.3934/mbe.2023804
Health literacy refers to the ability of individuals to obtain and understand health information and use it to maintain and promote their own health. This paper manages to make predictions toward its development degree in society with use of a big data-driven statistical learning method. Actually, such results can be analyzed by discovering latent rules from massive public textual contents. As a result, this paper proposes a deep information fusion-based smart prediction approach for health literacy. Specifically, the latent Dirichlet allocation (LDA) and convolutional neural network (CNN) structures are utilized as the basic backbone to understand semantic features of textual contents. The feature learning results of LDA and CNN can be then mapped into prediction results via following multi-dimension computing structures. After constructing the CNN model, we can input health information into the model for feature extraction. The CNN model can automatically learn valuable features from raw health information through multi-layer convolution and pooling operations. These characteristics may include lifestyle habits, physiological indicators, biochemical indicators, etc., reflecting the patient's health status and disease risk. After extracting features, we can train the CNN model through a training set and evaluate the performance of the model using a test set. The goal of this step is to optimize the parameters of the model so that it can accurately predict health information. We can use common evaluation indicators such as accuracy, precision, recall, etc. to evaluate the performance of the model. At last, some simulation experiments are conducted on real-world data collected from famous international universities. The case study analyzes health literacy difference between China of developed countries. Some prediction results can be obtained from the case study. The proposed approach can be proved effective from the discussion of prediction results.
| [1] |
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., 26 (2022), 5817–5828. https://doi.org/10.1109/JBHI.2021.3139541 doi: 10.1109/JBHI.2021.3139541
|
| [2] |
Q. Li, L. Liu, Z. Guo, P. Vijayakumar, F. Taghizadeh-Hesary, K. Yu, Smart assessment and forecasting framework for healthy development index in urban cities, Cities, 131 (2022), 103971. https://doi.org/10.1016/j.cities.2022.103971 doi: 10.1016/j.cities.2022.103971
|
| [3] |
N. C. Oleck, A. R. Johnson, B. N. N. Tran, H. S. Ayyala, E. S. Lee, B. T. Lee, A multimetric health literacy analysis of online information for gluteal augmentation with fat grafting, Ann. Plast. Surg., 85 (2020), S97–S101. https://doi.org/10.1097/SAP.0000000000002425 doi: 10.1097/SAP.0000000000002425
|
| [4] |
L. Yang, Y. Li, S. X. Yang, Y. Lu, T. Guo, K. Yu, Generative adversarial learning for intelligent trust management in 6G wireless networks, IEEE Network, 36 (2022), 134–140. https://doi.org/10.1109/MNET.003.2100672 doi: 10.1109/MNET.003.2100672
|
| [5] |
L. Zhao, Z. Yin, K. Yu, X. Tang, L. Xu, Z. Guo, et al., A fuzzy logic based intelligent multi-attribute routing scheme for two-layered SDVNs, IEEE Trans. Network Serv. Manage., 19 (2022), 4189–4200. https://doi.org/10.1109/TNSM.2022.3202741 doi: 10.1109/TNSM.2022.3202741
|
| [6] |
G. Kampouroglou, V. S. Velonaki, I. Pavlopoulou, E. Drakou, M. Kosmopoulos, N. Kouvas, et al., Parental anxiety in pediatric surgery consultations: The role of health literacy and need for information, J. Pediatric Surg., 55 (2020), 590–596. https://doi.org/10.1016/j.jpedsurg.2019.07.016 doi: 10.1016/j.jpedsurg.2019.07.016
|
| [7] | C. C. Cutilli, Health Literacy, Health Disparities and Sources of Health Information in US Older Adults, Ph.D thesis, Duquesne University, 2015. |
| [8] |
Z. Guo, K. Yu, N. Kumar, W. Wei, S. Mumtaz, M. Guizani, Deep distributed learning-based poi recommendation under mobile edge networks, IEEE Internet Things J., 10 (2023), 303–317. https://doi.org/10.1109/JIOT.2022.3202628 doi: 10.1109/JIOT.2022.3202628
|
| [9] | J. Zhang, L. Zhao, K. Yu, G. Min, A. Y. Al-Dubai, A. Y. Zomaya, A novel federated learning scheme for generative adversarial networks, IEEE Trans. Mobile Comput., 2023 (2023). https://doi.org/10.1109/TMC.2023.3278668 |
| [10] |
B. St. Jean, N. Greene Taylor, C. Kodama, M. Subramaniam, Assessing the health information source perceptions of tweens using card-sorting exercises, J. Inf. Sci., 44 (2018), 148–164. https://doi.org/10.1177/0165551516687728 doi: 10.1177/0165551516687728
|
| [11] |
B. Parlak, A. K. Uysal, The effects of globalisation techniques on feature selection for text classification, J. Inf. Sci., 47 (2021), 727–739. https://doi.org/10.1177/0165551520930897 doi: 10.1177/0165551520930897
|
| [12] |
K. Saho, K. Sugano, M. Kita, K. Uemura, M. Matsumoto, Classification of health literacy and cognitive impairments using higher-order kinematic parameters of the sit-to-stand movement from a monostatic doppler radar, IEEE Sensors J., 21 (2021), 10183–10192. https://doi.org/10.1109/JSEN.2021.3060050 doi: 10.1109/JSEN.2021.3060050
|
| [13] |
Y. J. Yi, B. Hwang, H. Yoon, H. Jeong, Health literacy and health information-seeking behavior of immigrants in south korea, Library Inf. Sci. Res., 43 (2021), 101121. https://doi.org/10.1016/j.lisr.2021.101121 doi: 10.1016/j.lisr.2021.101121
|
| [14] | Q. He, Z. Feng, H. Fang, X. Wang, L. Zhao, Y. Yao, et al., A blockchain-based scheme for secure data offloading in healthcare with deep reinforcement learning, IEEE/ACM Trans. Networking, 2023 (2023). https://doi.org/10.1109/TNET.2023.3274631 |
| [15] |
A. L. Neves, L. Freise, L. Laranjo, A. W. Carter, A. Darzi, E. Mayer, Impact of providing patients access to electronic health records on quality and safety of care: A systematic review and meta-analysis, BMJ Quality Saf., 29 (2020), 1019–1032. https://doi.org/10.1136/bmjqs-2019-010581 doi: 10.1136/bmjqs-2019-010581
|
| [16] |
A. McNeil, R. Arena, The evolution of health literacy and communication: introducing health harmonics, Prog. Cardiovasc. Dis., 59 (2017), 463–470. https://doi.org/10.1016/j.pcad.2017.02.003 doi: 10.1016/j.pcad.2017.02.003
|
| [17] |
Z. Guo, D. Meng, C. Chakraborty, X. R. Fan, A. Bhardwaj, K. Yu, Autonomous behavioral decision for vehicular agents based on cyber-physical social intelligence, IEEE Trans. Comput. Soc. Syst., 10 (2022), 2111–2122. https://doi.org/10.1109/TCSS.2022.3212864 doi: 10.1109/TCSS.2022.3212864
|
| [18] |
J. Zhang, Q. Yan, X. Zhu, K. Yu, Smart industrial iot empowered crowd sensing for safety monitoring in coal mine, Digital Commun. Networks, 9 (2023), 296–305. https://doi.org/10.1016/j.dcan.2022.08.002 doi: 10.1016/j.dcan.2022.08.002
|
| [19] |
A. De Thurah, Sp0008 the future for health professionals in rheumatology, Ann. Rheum. Dis., 76 (2017), 3. https://doi.org/10.1136/annrheumdis-2017-eular.7155 doi: 10.1136/annrheumdis-2017-eular.7155
|
| [20] | Z. Zhou, Y. Su, J. Li, K. Yu, Q. M. J. Wu, Z. Fu, et al., Secret-to-image reversible transformation for generative steganography, IEEE Trans. Dependable Secure Comput., 2022 (2022). https://doi.org/10.1109/TDSC.2022.3217661 |
| [21] |
S. S. Coughlin, J. L. Stewart, L. Young, V. Heboyan, G. De Leo, Health literacy and patient web portals, Int. J. Med. Inf., 113 (2018), 43–48. https://doi.org/10.1016/j.ijmedinf.2018.02.009 doi: 10.1016/j.ijmedinf.2018.02.009
|
| [22] |
R. Garcia-Retamero, E. T. Cokely, Designing visual aids that promote risk literacy: A systematic review of health research and evidence-based design heuristics, Hum. Factors, 59 (2017), 582–627. https://doi.org/10.1177/0018720817690634 doi: 10.1177/0018720817690634
|
| [23] |
Y. Zhang, Y. Sun, Y. Kim, The influence of individual differences on consumer's selection of online sources for health information, Comput. Hum. Behav., 67 (2017), 303–312. https://doi.org/10.1016/j.chb.2016.11.008 doi: 10.1016/j.chb.2016.11.008
|
| [24] |
H. Wang, N. Wang, M. Li, S. Mi, Y. Shi, Student physical health information management model under big data environment, Sci. Program., 2021 (2021), 1–10. https://doi.org/10.1155/2021/5795884 doi: 10.1155/2021/5795884
|
| [25] |
Z. Zhou, X. Dong, R. Meng, M. Wang, H. Yan, K. Yu, et al., Generative steganography via auto-generation of semantic object contours, IEEE Trans. Inf. Forensics Secu., 18 (2023), 2751–2765. https://doi.org/10.1109/TIFS.2023.3268843 doi: 10.1109/TIFS.2023.3268843
|
| [26] |
H. Xue, D. Chen, N. Zhang, H. Dai, K. Yu, Integration of blockchain and edge computing in internet of things: A survey, Future Gener. Comput. Syst., 144 (2023), 307–326. https://doi.org/10.1016/j.future.2022.10.029 doi: 10.1016/j.future.2022.10.029
|
| [27] |
M. Grene, Y. Cleary, A. Marcus-Quinn, Use of plain-language guidelines to promote health literacy, IEEE Trans. Prof. Commun., 60 (2017), 384–400. https://doi.org/10.1109/TPC.2017.2761578 doi: 10.1109/TPC.2017.2761578
|
| [28] |
T. P. Liang, Y. H. Liu, Research landscape of business intelligence and big data analytics: A bibliometrics study, Expert Syst. Appl., 111 (2018), 2–10. https://doi.org/10.1016/j.eswa.2018.05.018 doi: 10.1016/j.eswa.2018.05.018
|
| [29] |
F. Huang, L. Wang, H. Jia, Research trends for papillary thyroid carcinoma from 2010 to 2019: A systematic review and bibliometrics analysis, Medicine, 100 (2021), e26100. https://doi.org/10.1097/MD.0000000000026100 doi: 10.1097/MD.0000000000026100
|
| [30] |
M. M. Mirończuk, J. Protasiewicz, A recent overview of the state-of-the-art elements of text classification, Expert Syst. Appl., 106 (2018), 36–54. https://doi.org/10.1016/j.eswa.2018.03.058 doi: 10.1016/j.eswa.2018.03.058
|
| [31] |
S. Burkhardt, S. Kramer, Online multi-label dependency topic models for text classification, Mach. Learn., 107 (2018), 859–886. https://doi.org/10.1007/s10994-017-5689-6 doi: 10.1007/s10994-017-5689-6
|
| [32] |
F. Lei, X. Liu, Z. Li, Q. Dai, S. Wang, Multihop neighbor information fusion graph convolutional network for text classification, Math. Prob. Eng., 2021 (2021), 1–9. https://doi.org/10.1155/2021/6665588 doi: 10.1155/2021/6665588
|
Figures(9) / Tables(3)
Xiaoyan Zhao, Sanqing Ding. A multi-dimension information fusion-based intelligent prediction approach for health literacy[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 18104-18122. doi: 10.3934/mbe.2023804
DownLoad: