Proceedings of the 2025 5th International Conference on Informatization Economic Development and Management (IEDM 2025)

<Previous Article In Volume
Next Article In Volume>

Effects of Online Medical Teams on Patients’ Choices for Doctor Selection: A Hybrid Deep Learning Framework

Authors
Yongbo Ni1, *, Donghui Yang1
1Department of Management Science and Engineering, Southeast University, Nanjing, China
*Corresponding author. Email: nybgygc@163.com
Corresponding Author
Yongbo Ni
Available Online 26 May 2025.
DOI
10.2991/978-94-6463-724-3_5How to use a DOI?
Keywords
Online Healthcare Community; Online Medical Team; Doctor Selection; Deep Learning
Abstract

Amidst the overwhelming online medical service information, patients without professional medical knowledge often struggle to identify suitable doctors in online healthcare communities. The advent of online medical teams (OMTs) as a new source of publicly available information, offers patients novel avenues to understand the features of doctors. Therefore, in this study, we aim to explore the effect of OMTs information on patients’ choices for doctor selection, thereby better assisting patients in selecting a suitable doctor. We first integrate the OMTs information with the online reviews, disease descriptions, and doctor profiles to build the multi-source and multi-type medical data inputs. Based on these inputs, we develop a hybrid deep learning framework to uncover the effects of various factors on predicting patients’ choices for doctor selection. The results indicate that OMTs information can significantly enhance the probability of predicting patients’ choices for doctor selection. Surprisingly, the effect of OMTs information on patients’ preference features is greater than that of patients’ disease features in the process of doctor selection.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2025 5th International Conference on Informatization Economic Development and Management (IEDM 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
26 May 2025
ISBN
978-94-6463-724-3
ISSN
2352-5428
DOI
10.2991/978-94-6463-724-3_5How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yongbo Ni
AU  - Donghui Yang
PY  - 2025
DA  - 2025/05/26
TI  - Effects of Online Medical Teams on Patients’ Choices for Doctor Selection: A Hybrid Deep Learning Framework
BT  - Proceedings of the 2025 5th International Conference on Informatization Economic Development and Management (IEDM 2025)
PB  - Atlantis Press
SP  - 43
EP  - 57
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-724-3_5
DO  - 10.2991/978-94-6463-724-3_5
ID  - Ni2025
ER  -