Academic Journal

PREDICTING CONSULTATION SUCCESS ON ONLINE HEALTH PLATFORMS USING DYNAMIC KNOWLEDGE NETWORKS AND MULTIMODAL DATA FUSION.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: PREDICTING CONSULTATION SUCCESS ON ONLINE HEALTH PLATFORMS USING DYNAMIC KNOWLEDGE NETWORKS AND MULTIMODAL DATA FUSION.
Συγγραφείς: Geng, Shuang (AUTHOR) gs@szu.edu.cn, Zhang, Wenli (AUTHOR) wlzhang@iastate.edu, Xie, Jiaheng (AUTHOR) jxie@udel.edu, Liang, Gemin (AUTHOR) gemin@bit.edu.cn, Niu, Ben (AUTHOR) drniuben@gmail.com, Ram, Sudha (AUTHOR) sram@arizona.edu
Πηγή: MIS Quarterly. Sep2026, Vol. 50 Issue 3, p909-940. 32p. 2 Color Photographs, 3 Diagrams, 12 Charts, 2 Graphs.
Θεματικοί όροι: *Digital platforms, *Forecasting, *Artificial neural networks, Digital health, Data fusion (Statistics), Medical consultation, Patients
Περίληψη: Online healthcare consultation in virtual health is an emerging industry marked by innovation and fierce competition. Accurate and early prediction of healthcare consultation success can help online platforms proactively address patient concerns and improve retention rates. However, this prediction task is inherently challenging due to several factors: Patients' needs often remain unclear until they explicitly articulate them, and their questions may evolve throughout the consultation process. Additionally, the task involves processing multimodal input information, including consultation dialogues and the complex network of various stakeholders in a patient's healthcare journey. To address these issues, we propose the "dynamic knowledge network and multimodal data fusion" framework with a dynamic knowledge graph and multimodal data fusion, which enhances the predictive power of online healthcare consultations. Our work has important implications for new business models where specific and detailed online communication processes are stored in the IT database, and at the same time, latent information with predictive power is embedded in the network formed by stakeholders' digital traces. It can be extended to diverse industries and domains, where the virtual or hybrid model (e.g., integration of online and offline services) is emerging as a prevailing trend. [ABSTRACT FROM AUTHOR]
Copyright of MIS Quarterly is the property of MIS Quarterly and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Business Source Index