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A Deep Learning-Based Method for Similar Patient Question Retrieval in Chinese.

Guo Yu Tang1, Yuan Ni1, Guo Tong Xie1

  • 1IBM Research, China, Beijing, China.

Studies in Health Technology and Informatics
|January 4, 2018
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Summary

This study introduces a novel deep learning method to quickly find similar patient questions in Chinese online Q&A archives. The supervised neural attention (SNA) model significantly improves retrieval accuracy, aiding faster patient support.

Keywords:
Natural Language ProcessingNeural Networks (Computer)Supervised Machine Learning

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Health Informatics

Background:

  • Online patient question and answering (Q&A) systems are increasingly popular in China.
  • Existing systems face challenges in quickly retrieving relevant information from large archives.
  • Patients often wait for doctor responses, highlighting the need for efficient information retrieval.

Purpose of the Study:

  • To develop a novel deep learning method for retrieving semantically similar patient questions in Chinese.
  • To improve the efficiency of online Q&A systems by enabling quick answers from archived questions.
  • To evaluate the performance of the proposed method against existing approaches.

Main Methods:

  • Utilized unsupervised learning with deep neural networks to generate word embeddings from a Chinese corpus.
  • Developed a supervised learning algorithm, the supervised neural attention (SNA) model, to predict question similarity.
  • Employed word embeddings as input for the SNA model to assess semantic equivalence between questions.

Main Results:

  • The SNA method achieved a precision of 77% at the first position (P@1) and 84% at the fifth position (P@5).
  • The proposed SNA method significantly outperformed all other compared retrieval methods.
  • Demonstrated the effectiveness of deep learning and attention mechanisms in semantic similarity tasks for Chinese medical questions.

Conclusions:

  • The novel supervised neural attention (SNA) model offers a highly effective solution for retrieving similar patient questions in Chinese.
  • This approach can enhance the user experience and efficiency of online health Q&A platforms.
  • Deep learning-based methods show great promise for advancing medical information retrieval systems.