Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Natural Language Processing

Background:

• Online patient question and answering (Q&A) systems are increasingly popular in China.
• Current systems face challenges with response lag time and doctor workload.
• Efficient retrieval of semantically equivalent questions from archives is needed.

Purpose of the Study:

• To develop an automated method for retrieving semantically equivalent patient questions.
• To reduce response delays and alleviate the burden on medical professionals.
• To enhance the performance of existing deep learning models using Generative Adversarial Networks (GANs).

Main Methods:

• Utilized supervised deep learning approaches to assess patient question similarity.
• Implemented a Generative Adversarial Network (GAN) framework to fine-tune pre-trained deep learning models.
• Focused on automatic retrieval of patient questions within an online Q&A context.

Main Results:

• The Generative Adversarial Network (GAN) based fine-tuning significantly improved the performance of the question retrieval system.
• The approach demonstrated effectiveness in identifying semantically similar patient questions.
• Experimental results confirmed the benefits of GAN fine-tuning over standard deep learning methods.

Conclusions:

• Generative Adversarial Networks (GANs) offer a promising method for enhancing automated patient question retrieval.
• This technique can improve the efficiency and effectiveness of online medical Q&A platforms.
• The study highlights the potential of advanced AI techniques to optimize healthcare information systems.