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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs.

Yongjie Yan1,2, Guang Yu1, Xiangbin Yan3

  • 1School of Management, Harbin Institute of Technology, Harbin 150001, China.

Computational Intelligence and Neuroscience
|October 30, 2020
PubMed
Summary

This study introduces a novel recommendation system, Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN), to help patients find suitable doctors online. The PMF-CNN model enhances medical consultation recommendations by effectively combining patient reviews and doctor data.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Information Retrieval

Background:

  • Online medical communities are crucial for connecting patients with healthcare providers.
  • Existing recommendation systems often struggle to effectively utilize diverse data sources like patient reviews and doctor profiles.
  • Integrating internet technology with traditional medical services requires advanced recommendation solutions.

Purpose of the Study:

  • To develop and evaluate a novel recommendation model for online medical consultation platforms.
  • To improve the accuracy and efficiency of matching patients with appropriate doctors and hospitals.
  • To enhance the utilization of medical resources through intelligent recommendations.

Main Methods:

  • Proposed a new recommendation model: Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN).
  • Utilized Convolutional Neural Networks (CNN) to extract feature representations from review text and doctor information.
  • Employed an extended matrix factorization approach to fuse review features and initial doctor data for improved recommendations.

Main Results:

  • The PMF-CNN model demonstrated superior recommendation performance compared to existing state-of-the-art algorithms on the Haodf.com dataset.
  • The system effectively fuses review text and doctor information, leading to better medical consultation recommendations.
  • Experimental results confirm the model's ability to enhance recommendation accuracy.

Conclusions:

  • The developed PMF-CNN recommendation system significantly improves patient-doctor matching in online medical consultations.
  • The system contributes to better utilization of doctors and a more balanced allocation of public health resources.
  • This approach facilitates the integration of digital technology into traditional healthcare services.