research-article

A Doctor Recommendation Framework for Online Medical Platforms Using Multi-Source Heterogeneous Data

Authors:

Bo JinAuthors Info & Claims

ICCAI '21: Proceedings of the 2021 7th International Conference on Computing and Artificial Intelligence

Pages 326 - 331

https://doi.org/10.1145/3467707.3467757

Published: 24 September 2021 Publication History

Abstract

The emergence of the online medical platform provides convenience for patients, but at the same time, how to choose the right doctor among thousands of doctors on the platform has become a problem for patients. Nowadays, most doctor recommendation methods meet computation inefficiency issues as the data amount is extreme huge. In addition, the data on different medical platforms cannot be utilized due to inconsistent structures. To address the above two problems, this paper proposes a framework for doctor recommendation using multi-source heterogeneous data with two models: knowledge fusion model and doctor recommendation model. In the knowledge fusion model, two systems are established for co-evolution to integrate multi-source heterogeneity data. One system maps various types of health data to coding strings for evolutionary calculation; the other uses co-evolution to generate a unified coding string. In the doctor recommendation model, the ranking of doctors under different attributes is used as input to obtain the comprehensive ranking of doctors, the method performs effective to fuse knowledge from all kinds of data. The experimental results on 1157 orthopedic surgeons show that our framework greatly improves the computational efficiency while maintaining the accuracy.

References

[1]

Hu G., Han X., Zhou H., and Liu Y. 2019. Public perception on healthcare services: evidence from social media platforms in China. International Journal of Environmental Research and Public Health 16, 7 (April 2019), 1273. https://doi.org/10.3390/ijerph16071273

[2]

Safikureshi Mondal, Anwesha Basu, and Nandini Mukherjee. 2020. Building a trust-based doctor recommendation system on top of multilayer graph database. Journal of Biomedical Informatics, Vol. 110. https://doi.org/10.1016/j.jbi.2020.103549

Digital Library

[3]

Gao R., Li J., Li X., Song C., and Zhou Y. 2017. A personalized point-of-interest recommendation model via fusion of geosocial information. Neurocomputing 273 (August 2017), 159–170. https://doi.org/10.1016/j.neucom.2017.08.020

Digital Library

[4]

Alexander Smirnov, and Tatiana Levashova. 2019. Knowledge fusion patterns: A survey. Information Fusion 52, (December 2019), 31-40. https://doi.org/10.1016/j.inffus.2018.11.007

Digital Library

[5]

Yu S., Tranchevent L.C., and De Moor B. 2011. Kernel-based data fusion for machine learning. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-19406-1

[6]

Xu Man and Shen Jiang. 2013. Bayesian Based Information Fusion and Its Application in Heart Disease Diagnosis. Industrial Engineering and Management 18, 4, 130-137.

[7]

James A.P., and Dasarathy B.V. 2014. Medical image fusion: A survey of the state of the art. Information Fusion 19, (December 2013), 4-19. https://doi.org/10.1016/j.inffus.2013.12.002

Digital Library

[8]

Singh R. and Khare A. 2014. Fusion of multimodal medical images using Daubechies complex wavelet transform–A multiresolution approach. Information Fusion 19, (January 2012), 49-60. https://doi.org/10.1016/j.inffus.2012.09.005

Digital Library

[9]

Yang Y., Park D.S., and Huang S. 2010. Medical image fusion via an effective wavelet-based approach. EURASIP Journal on Advances in Signal Processing 2010, 8 (December 2010), 1-13. https://doi.org/10.1155/2010/579341

[10]

Yang Y.C., Dang J.W., and Wang Y.P. 2012. A Medical Image Fusion Method Based on Lifting Wavelet Transform and Adaptive PCNN. Journal of Computer-Aided Design & Computer Graphics 24, 4 (April 2012), 494-499.

[11]

Hao C.L., Song Y.Q., and Zhou Q.H. 2013. Medical Image Fusion Algorithm Based on Rough Sets. Computer Measurement & Control 21, 9, 2532-2534.

[12]

Wang L.C., Meng X.W., and Zhang Y.J. 2012. Context-Aware Recommender Systems. Journal of Software 23, 1 (January 2012), 1-20. https://doi.org/10.3724/SP.J.1001.2012.04100

[13]

Ma G.W. 2015. A Study of User Recommendation Algorithms Based on Social Influence Analysis. HeFei: University of Science and Technology of China, (2015), 1-55.

[14]

Ekstrand M.D., Riedl J.T., and Konstan J.A. 2011. Collaborative filtering recommender systems. Foundations and Trends® in Human–Computer Interaction 4321, (2011), 291-324. https://doi.org/10.1561/1100000009

Digital Library

[15]

Davis D.A., Chawla N.V., and Christakis N.A. 2010. Time to CARE: a collaborative engine for practical disease prediction. Data Mining and Knowledge Discovery 20, 3 (May 2009), 388-415. https://doi.org/10.1007/s10618-009-0156-z

Digital Library

[16]

Davis M.E. and Shin D.M. 2008. Nanoparticle therapeutics: an emerging treatment modality for cancer. Nature reviews Drug discovery 7, 9 (October 2008), 771-782. https://doi.org/10.1038/nrd2614

[17]

Hassan A.K.M., Bergheanu S.C., and Stijnen T. 2010. Late stent malapposition risk is higher after drug-eluting stent compared with bare-metal stent implantation and associates with late stent thrombosis. European heart journal 31, 9 (February 2009), 1172-1180. https://doi.org/10.1093/eurheartj/ehn553

[18]

Zheng H., Padman R., and Neill D.B. 2010. A Comparison of Collaborative Filtering Methods for Medication Reconciliation. in Cape Town, South Africa. In: Proceedings of the 13th International Congress on Medical Informatics, (January 2010), 68-76.

Cited By

SHAMBOUR QABUALHAJ MSHAREHA A(2024)An Effective Doctor Recommendation Algorithm for Online Healthcare PlatformsRomanian Journal of Information Science and Technology10.59277/ROMJIST.2024.1.062024:1(81-93)Online publication date: 26-Mar-2024
https://doi.org/10.59277/ROMJIST.2024.1.06
Thakur G(2023)An Integration and Mining of Multiple-Source Data for Real-Time Services2023 4th IEEE Global Conference for Advancement in Technology (GCAT)10.1109/GCAT59970.2023.10353303(1-7)Online publication date: 6-Oct-2023
https://doi.org/10.1109/GCAT59970.2023.10353303

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Abstract
A well-performed doctor recommendation system is significant for both patients and Online Medical Consultation Platforms (OMCPs). Though previous studies have proposed many doctor recommendation methods, some are overly personalized for ...

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ICCAI '21: Proceedings of the 2021 7th International Conference on Computing and Artificial Intelligence

April 2021

498 pages

ISBN:9781450389501

DOI:10.1145/3467707

Copyright © 2021 ACM.

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Publication History

Published: 24 September 2021

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the National Natural Science Foundation of China

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ICCAI '21

ICCAI '21: 2021 7th International Conference on Computing and Artificial Intelligence

April 23 - 26, 2021

Tianjin, China

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Cited By

SHAMBOUR QABUALHAJ MSHAREHA A(2024)An Effective Doctor Recommendation Algorithm for Online Healthcare PlatformsRomanian Journal of Information Science and Technology10.59277/ROMJIST.2024.1.062024:1(81-93)Online publication date: 26-Mar-2024
https://doi.org/10.59277/ROMJIST.2024.1.06
Thakur G(2023)An Integration and Mining of Multiple-Source Data for Real-Time Services2023 4th IEEE Global Conference for Advancement in Technology (GCAT)10.1109/GCAT59970.2023.10353303(1-7)Online publication date: 6-Oct-2023
https://doi.org/10.1109/GCAT59970.2023.10353303

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