Doctor-Patient Compatibility in Online Healthcare Based on Dynamic Fusion of Multimodal Uncertainty Information
Authors: 
Haoxin Tang, Decui LiangAuthors Info & Claims
Haoxin Tang, Decui LiangAuthors Info & ClaimsPages 71 - 75
Abstract
With the reopening post-pandemic and the advancement of smart healthcare, online consultations have gradually become one of the primary means of health management due to their convenience and low cost. However, the uncertainty in different modalities between patients and doctors has further complicated the challenge of matching. How to achieve efficient patient-doctor matching has become a pressing issue. Therefore, this paper proposes a multimodal uncertainty dynamic fusion method. First, we design a method for measuring uncertainty based on the modality information of each doctor and patient. Then, considering the upper bound of the model's error, we introduce a regularization term between modality weights and loss to improve the loss function. Finally, we conduct model comparisons and uncertainty information fusion analysis using the data provided by the Haodaifu online medical platform. The results demonstrate the effectiveness of the proposed method.
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Index Terms
- Doctor-Patient Compatibility in Online Healthcare Based on Dynamic Fusion of Multimodal Uncertainty Information
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Published In
September 2024
384 pages
ISBN:9798400710148
DOI:10.1145/3702879
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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Association for Computing Machinery
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Published: 11 December 2024
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IoTCCT 2024
IoTCCT 2024: 2024 2nd International Conference on Internet of Things and Cloud Computing Technology
September 27 - 29, 2024
Paris, France
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