Real-time clinical analytics at scale: a platform built on large language models-powered knowledge graphs

Area of Science:

• Biomedical Informatics
• Clinical Data Analytics
• Artificial Intelligence in Healthcare

Background:

• The exponential growth of clinical trial data overwhelms traditional analysis methods.
• Analyzing vast, unstructured clinical documents and electronic medical records is a major challenge for researchers.

Purpose of the Study:

• To develop a scalable platform for real-time clinical analytics.
• To address the limitations of existing document-centric and retrieval-based methods.

Main Methods:

• Integration of Large Language Models (LLMs) with Knowledge Graph technology.
• Implementation of a 2-phase graph update strategy and hardware acceleration.
• Real-time analytics on over 110,000 clinical documents and 60,000 electronic medical records.

Main Results:

• Achieved an average query delay of 1.7 seconds with high accuracy (BLEU: 0.85, ROUGE: 0.92).
• Demonstrated real-time processing and analysis of thousands of clinical documents.
• Significantly outperformed existing clinical analytics methods.

Conclusions:

• Combining LLMs and continuously updated knowledge graphs enables scalable, low-latency clinical analytics.
• The platform supports real-time clinical decision-making and large-scale evidence synthesis.
• Offers an efficient solution for rapid analysis of extensive clinical document collections.