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Adaptive Top-K Algorithm for Medical Conversational Diagnostic Model.

Yiqing Yang1, Guoyin Zhang1, Yanxia Wu1

  • 1Department of Computer Science, Harbin Engineering University, Harbin 150001, China.

Entropy (Basel, Switzerland)
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized Top-K algorithm for conversational diagnostic systems, improving disease prediction accuracy to 99.81%. The enhanced machine learning model speeds up telemedicine diagnostics by 1.3-1.9 times.

Keywords:
Top-K algorithmdifferential diagnosis systemsreinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Digital Healthcare
  • Machine Learning

Background:

  • Conversational diagnostic systems leverage machine learning for disease prediction.
  • Traditional systems focus on single disease diagnosis, unlike clinical differential diagnosis.
  • Differential diagnosis in acute situations requires speed, accuracy, and managing computational complexity.

Purpose of the Study:

  • To optimize the Top-K algorithm for enhanced efficiency and accuracy in telemedicine diagnostic systems.
  • To improve the diagnostic process by dynamically adjusting disease and symptom considerations.
  • To reduce computational costs while maintaining high diagnostic performance.

Main Methods:

  • Development of an optimized Top-K algorithm for differential diagnosis.
  • Dynamic adjustment of likely diseases and symptoms based on real-time case progress.
  • Optimization of the diagnostic model using a policy network loss function.

Main Results:

  • Achieved a diagnostic accuracy of 99.81%.
  • Increased the exclusion rate of severe pathologies.
  • Improved system response speed by 1.3-1.9 times compared to state-of-the-art systems.
  • Reduced the number of symptoms and diseases processed.

Conclusions:

  • The optimized Top-K algorithm significantly enhances telemedicine diagnostic systems.
  • The algorithm improves both diagnostic accuracy and response speed.
  • This approach effectively addresses the challenges of differential diagnosis in digital healthcare.