Medical Intelligent System and Orthopedic Clinical Nursing Based on Graph Partition Sampling Algorithm.

Area of Science:

• Computer Science
• Medical Informatics
• Graph Theory

Background:

• Graph partition sampling algorithms are crucial for efficient data processing.
• Remote medical intelligence systems require robust hardware, software, and network infrastructure.
• Orthopedic patients need specialized care due to limitations in mobility and self-care.

Purpose of the Study:

• To summarize and analyze graph partition sampling algorithms.
• To design a medical intelligence system for remote patient-doctor-nurse communication.
• To improve orthopedic clinical nursing management through technology.

Main Methods:

• Comparative analysis of various graph partition sampling algorithms.
• Development of a medical intelligence system integrating hardware, software, and network components.
• Definition of audio/video encoding standards for remote consultations.

Main Results:

• The learning process of graph sampling is structured into three distinct stages.
• A functional medical intelligence system was developed, enabling remote connections.
• The system aims to provide personalized medical guidance to orthopedic patients.

Conclusions:

• The study provides a framework for understanding graph partition sampling.
• The developed medical intelligence system enhances remote healthcare delivery for orthopedic patients.
• Integrating advanced computing and network technology improves patient care and management.