Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Data Science

Background:

• Information technology advancements are transforming data management and analysis across industries.
• Deep learning algorithms offer potential for enhancing disease recognition accuracy in medicine.
• Limited medical resources necessitate innovative solutions for equitable healthcare access.

Purpose of the Study:

• To develop an intelligent medical service platform for resource sharing and improved disease diagnosis.
• To leverage deep learning and advanced algorithms for accurate medical data analysis and auxiliary diagnosis.
• To address the challenge of scarce medical resources through technological solutions.

Main Methods:

• Utilized Digital Twins module within Deep Learning for medical care and auxiliary diagnosis models.
• Employed Internet of Things (IoT) technology for digital visualization and data collection (client/server).
• Implemented an improved Random Forest algorithm (RW-RF) for demand analysis, target function design, and system development.

Main Results:

• The intelligent medical service platform effectively collects and analyzes clinical trial data.
• The improved ReliefF & Wrapper Random Forest (RW-RF) algorithm achieved approximately 98% accuracy for sepsis recognition.
• The developed algorithm demonstrated over 80% accuracy for general disease recognition.

Conclusions:

• The intelligent medical service platform provides robust technical support for disease recognition and medical care.
• The study offers a practical solution and experimental reference for overcoming medical resource scarcity.
• The findings highlight the potential of AI-driven platforms to enhance healthcare delivery and accessibility.